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Procedural terrain generation with style transfer (2403.08782v1)

Published 28 Jan 2024 in cs.CV and cs.AI

Abstract: In this study we introduce a new technique for the generation of terrain maps, exploiting a combination of procedural generation and Neural Style Transfer. We consider our approach to be a viable alternative to competing generative models, with our technique achieving greater versatility, lower hardware requirements and greater integration in the creative process of designers and developers. Our method involves generating procedural noise maps using either multi-layered smoothed Gaussian noise or the Perlin algorithm. We then employ an enhanced Neural Style transfer technique, drawing style from real-world height maps. This fusion of algorithmic generation and neural processing holds the potential to produce terrains that are not only diverse but also closely aligned with the morphological characteristics of real-world landscapes, with our process yielding consistent terrain structures with low computational cost and offering the capability to create customized maps. Numerical evaluations further validate our model's enhanced ability to accurately replicate terrain morphology, surpassing traditional procedural methods.

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References (16)
  1. Rose, T.J., Bakaoukas, A.G.: Algorithms and approaches for procedural terrain generation - a brief review of current techniques. In: 2016 8th International Conference on Games and Virtual Worlds for Serious Applications (VS-GAMES), pp. 1–2 (2016). https://doi.org/10.1109/VS-GAMES.2016.7590336 (3) Perlin, K.: Improving noise. In: Proceedings of the 29th Annual Conference on Computer Graphics and Interactive Techniques, pp. 681–682 (2002) (4) Doran, J., Parberry, I.: Controlled procedural terrain generation using software agents. IEEE Transactions on Computational Intelligence and AI in Games 2(2), 111–119 (2010). https://doi.org/10.1109/TCIAIG.2010.2049020 (5) Génevaux, J.-D., Galin, E., Guérin, E., Peytavie, A., Benes, B.: Terrain generation using procedural models based on hydrology. ACM Trans. Graph. 32(4) (2013). https://doi.org/10.1145/2461912.2461996 (6) Mei, X., Decaudin, P., Hu, B.-G.: Fast hydraulic erosion simulation and visualization on gpu. In: 15th Pacific Conference on Computer Graphics and Applications (PG’07), pp. 47–56 (2007). IEEE (7) Ong, T.J., Saunders, R., Keyser, J., Leggett, J.J.: Terrain generation using genetic algorithms. In: Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation. GECCO ’05, pp. 1463–1470. Association for Computing Machinery, New York, NY, USA (2005). https://doi.org/10.1145/1068009.1068241. https://doi.org/10.1145/1068009.1068241 (8) Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. Commun. ACM 63(11), 139–144 (2020). https://doi.org/10.1145/3422622 (9) Sisodia, Y.: Gan-generated terrain for game assets. Indian Journal of Artificial Intelligence and Neural Networking 2, 1–3 (2022). https://doi.org/10.54105/ijainn.F1060.102622 (10) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics (2023) (11) Beckham, C., Pal, C.: A step towards procedural terrain generation with gans. arXiv preprint arXiv:1707.03383 (2017) (12) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Perlin, K.: Improving noise. In: Proceedings of the 29th Annual Conference on Computer Graphics and Interactive Techniques, pp. 681–682 (2002) (4) Doran, J., Parberry, I.: Controlled procedural terrain generation using software agents. IEEE Transactions on Computational Intelligence and AI in Games 2(2), 111–119 (2010). https://doi.org/10.1109/TCIAIG.2010.2049020 (5) Génevaux, J.-D., Galin, E., Guérin, E., Peytavie, A., Benes, B.: Terrain generation using procedural models based on hydrology. ACM Trans. Graph. 32(4) (2013). https://doi.org/10.1145/2461912.2461996 (6) Mei, X., Decaudin, P., Hu, B.-G.: Fast hydraulic erosion simulation and visualization on gpu. In: 15th Pacific Conference on Computer Graphics and Applications (PG’07), pp. 47–56 (2007). IEEE (7) Ong, T.J., Saunders, R., Keyser, J., Leggett, J.J.: Terrain generation using genetic algorithms. In: Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation. GECCO ’05, pp. 1463–1470. Association for Computing Machinery, New York, NY, USA (2005). https://doi.org/10.1145/1068009.1068241. https://doi.org/10.1145/1068009.1068241 (8) Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. Commun. ACM 63(11), 139–144 (2020). https://doi.org/10.1145/3422622 (9) Sisodia, Y.: Gan-generated terrain for game assets. Indian Journal of Artificial Intelligence and Neural Networking 2, 1–3 (2022). https://doi.org/10.54105/ijainn.F1060.102622 (10) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics (2023) (11) Beckham, C., Pal, C.: A step towards procedural terrain generation with gans. arXiv preprint arXiv:1707.03383 (2017) (12) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Doran, J., Parberry, I.: Controlled procedural terrain generation using software agents. IEEE Transactions on Computational Intelligence and AI in Games 2(2), 111–119 (2010). https://doi.org/10.1109/TCIAIG.2010.2049020 (5) Génevaux, J.-D., Galin, E., Guérin, E., Peytavie, A., Benes, B.: Terrain generation using procedural models based on hydrology. ACM Trans. Graph. 32(4) (2013). https://doi.org/10.1145/2461912.2461996 (6) Mei, X., Decaudin, P., Hu, B.-G.: Fast hydraulic erosion simulation and visualization on gpu. In: 15th Pacific Conference on Computer Graphics and Applications (PG’07), pp. 47–56 (2007). IEEE (7) Ong, T.J., Saunders, R., Keyser, J., Leggett, J.J.: Terrain generation using genetic algorithms. In: Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation. GECCO ’05, pp. 1463–1470. Association for Computing Machinery, New York, NY, USA (2005). https://doi.org/10.1145/1068009.1068241. https://doi.org/10.1145/1068009.1068241 (8) Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. Commun. ACM 63(11), 139–144 (2020). https://doi.org/10.1145/3422622 (9) Sisodia, Y.: Gan-generated terrain for game assets. Indian Journal of Artificial Intelligence and Neural Networking 2, 1–3 (2022). https://doi.org/10.54105/ijainn.F1060.102622 (10) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics (2023) (11) Beckham, C., Pal, C.: A step towards procedural terrain generation with gans. arXiv preprint arXiv:1707.03383 (2017) (12) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Génevaux, J.-D., Galin, E., Guérin, E., Peytavie, A., Benes, B.: Terrain generation using procedural models based on hydrology. ACM Trans. Graph. 32(4) (2013). https://doi.org/10.1145/2461912.2461996 (6) Mei, X., Decaudin, P., Hu, B.-G.: Fast hydraulic erosion simulation and visualization on gpu. In: 15th Pacific Conference on Computer Graphics and Applications (PG’07), pp. 47–56 (2007). IEEE (7) Ong, T.J., Saunders, R., Keyser, J., Leggett, J.J.: Terrain generation using genetic algorithms. In: Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation. GECCO ’05, pp. 1463–1470. Association for Computing Machinery, New York, NY, USA (2005). https://doi.org/10.1145/1068009.1068241. https://doi.org/10.1145/1068009.1068241 (8) Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. Commun. ACM 63(11), 139–144 (2020). https://doi.org/10.1145/3422622 (9) Sisodia, Y.: Gan-generated terrain for game assets. Indian Journal of Artificial Intelligence and Neural Networking 2, 1–3 (2022). https://doi.org/10.54105/ijainn.F1060.102622 (10) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics (2023) (11) Beckham, C., Pal, C.: A step towards procedural terrain generation with gans. arXiv preprint arXiv:1707.03383 (2017) (12) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Mei, X., Decaudin, P., Hu, B.-G.: Fast hydraulic erosion simulation and visualization on gpu. In: 15th Pacific Conference on Computer Graphics and Applications (PG’07), pp. 47–56 (2007). IEEE (7) Ong, T.J., Saunders, R., Keyser, J., Leggett, J.J.: Terrain generation using genetic algorithms. In: Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation. GECCO ’05, pp. 1463–1470. Association for Computing Machinery, New York, NY, USA (2005). https://doi.org/10.1145/1068009.1068241. https://doi.org/10.1145/1068009.1068241 (8) Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. Commun. ACM 63(11), 139–144 (2020). https://doi.org/10.1145/3422622 (9) Sisodia, Y.: Gan-generated terrain for game assets. Indian Journal of Artificial Intelligence and Neural Networking 2, 1–3 (2022). https://doi.org/10.54105/ijainn.F1060.102622 (10) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics (2023) (11) Beckham, C., Pal, C.: A step towards procedural terrain generation with gans. arXiv preprint arXiv:1707.03383 (2017) (12) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Ong, T.J., Saunders, R., Keyser, J., Leggett, J.J.: Terrain generation using genetic algorithms. In: Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation. GECCO ’05, pp. 1463–1470. Association for Computing Machinery, New York, NY, USA (2005). https://doi.org/10.1145/1068009.1068241. https://doi.org/10.1145/1068009.1068241 (8) Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. Commun. ACM 63(11), 139–144 (2020). https://doi.org/10.1145/3422622 (9) Sisodia, Y.: Gan-generated terrain for game assets. Indian Journal of Artificial Intelligence and Neural Networking 2, 1–3 (2022). https://doi.org/10.54105/ijainn.F1060.102622 (10) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics (2023) (11) Beckham, C., Pal, C.: A step towards procedural terrain generation with gans. arXiv preprint arXiv:1707.03383 (2017) (12) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. Commun. ACM 63(11), 139–144 (2020). https://doi.org/10.1145/3422622 (9) Sisodia, Y.: Gan-generated terrain for game assets. Indian Journal of Artificial Intelligence and Neural Networking 2, 1–3 (2022). https://doi.org/10.54105/ijainn.F1060.102622 (10) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics (2023) (11) Beckham, C., Pal, C.: A step towards procedural terrain generation with gans. arXiv preprint arXiv:1707.03383 (2017) (12) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Sisodia, Y.: Gan-generated terrain for game assets. Indian Journal of Artificial Intelligence and Neural Networking 2, 1–3 (2022). https://doi.org/10.54105/ijainn.F1060.102622 (10) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics (2023) (11) Beckham, C., Pal, C.: A step towards procedural terrain generation with gans. arXiv preprint arXiv:1707.03383 (2017) (12) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics (2023) (11) Beckham, C., Pal, C.: A step towards procedural terrain generation with gans. arXiv preprint arXiv:1707.03383 (2017) (12) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Beckham, C., Pal, C.: A step towards procedural terrain generation with gans. arXiv preprint arXiv:1707.03383 (2017) (12) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). 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Computers & Graphics (2023) (11) Beckham, C., Pal, C.: A step towards procedural terrain generation with gans. arXiv preprint arXiv:1707.03383 (2017) (12) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Beckham, C., Pal, C.: A step towards procedural terrain generation with gans. arXiv preprint arXiv:1707.03383 (2017) (12) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. 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International journal of computer vision 115, 211–252 (2015) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). 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International journal of computer vision 115, 211–252 (2015) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. 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In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Sisodia, Y.: Gan-generated terrain for game assets. Indian Journal of Artificial Intelligence and Neural Networking 2, 1–3 (2022). https://doi.org/10.54105/ijainn.F1060.102622 (10) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. 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Computers & Graphics (2023) (11) Beckham, C., Pal, C.: A step towards procedural terrain generation with gans. arXiv preprint arXiv:1707.03383 (2017) (12) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). 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International journal of computer vision 115, 211–252 (2015) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). 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Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015)
  4. Génevaux, J.-D., Galin, E., Guérin, E., Peytavie, A., Benes, B.: Terrain generation using procedural models based on hydrology. ACM Trans. Graph. 32(4) (2013). https://doi.org/10.1145/2461912.2461996 (6) Mei, X., Decaudin, P., Hu, B.-G.: Fast hydraulic erosion simulation and visualization on gpu. In: 15th Pacific Conference on Computer Graphics and Applications (PG’07), pp. 47–56 (2007). IEEE (7) Ong, T.J., Saunders, R., Keyser, J., Leggett, J.J.: Terrain generation using genetic algorithms. In: Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation. GECCO ’05, pp. 1463–1470. Association for Computing Machinery, New York, NY, USA (2005). https://doi.org/10.1145/1068009.1068241. https://doi.org/10.1145/1068009.1068241 (8) Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. Commun. ACM 63(11), 139–144 (2020). https://doi.org/10.1145/3422622 (9) Sisodia, Y.: Gan-generated terrain for game assets. Indian Journal of Artificial Intelligence and Neural Networking 2, 1–3 (2022). https://doi.org/10.54105/ijainn.F1060.102622 (10) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics (2023) (11) Beckham, C., Pal, C.: A step towards procedural terrain generation with gans. arXiv preprint arXiv:1707.03383 (2017) (12) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Mei, X., Decaudin, P., Hu, B.-G.: Fast hydraulic erosion simulation and visualization on gpu. 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Association for Computing Machinery, New York, NY, USA (2005). https://doi.org/10.1145/1068009.1068241. https://doi.org/10.1145/1068009.1068241 (8) Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. Commun. ACM 63(11), 139–144 (2020). https://doi.org/10.1145/3422622 (9) Sisodia, Y.: Gan-generated terrain for game assets. Indian Journal of Artificial Intelligence and Neural Networking 2, 1–3 (2022). https://doi.org/10.54105/ijainn.F1060.102622 (10) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. 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In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Sisodia, Y.: Gan-generated terrain for game assets. Indian Journal of Artificial Intelligence and Neural Networking 2, 1–3 (2022). https://doi.org/10.54105/ijainn.F1060.102622 (10) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics (2023) (11) Beckham, C., Pal, C.: A step towards procedural terrain generation with gans. arXiv preprint arXiv:1707.03383 (2017) (12) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics (2023) (11) Beckham, C., Pal, C.: A step towards procedural terrain generation with gans. arXiv preprint arXiv:1707.03383 (2017) (12) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Beckham, C., Pal, C.: A step towards procedural terrain generation with gans. arXiv preprint arXiv:1707.03383 (2017) (12) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015)
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Association for Computing Machinery, New York, NY, USA (2005). https://doi.org/10.1145/1068009.1068241. https://doi.org/10.1145/1068009.1068241 (8) Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. Commun. ACM 63(11), 139–144 (2020). https://doi.org/10.1145/3422622 (9) Sisodia, Y.: Gan-generated terrain for game assets. Indian Journal of Artificial Intelligence and Neural Networking 2, 1–3 (2022). https://doi.org/10.54105/ijainn.F1060.102622 (10) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics (2023) (11) Beckham, C., Pal, C.: A step towards procedural terrain generation with gans. arXiv preprint arXiv:1707.03383 (2017) (12) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. Commun. ACM 63(11), 139–144 (2020). https://doi.org/10.1145/3422622 (9) Sisodia, Y.: Gan-generated terrain for game assets. Indian Journal of Artificial Intelligence and Neural Networking 2, 1–3 (2022). https://doi.org/10.54105/ijainn.F1060.102622 (10) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics (2023) (11) Beckham, C., Pal, C.: A step towards procedural terrain generation with gans. arXiv preprint arXiv:1707.03383 (2017) (12) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Sisodia, Y.: Gan-generated terrain for game assets. Indian Journal of Artificial Intelligence and Neural Networking 2, 1–3 (2022). https://doi.org/10.54105/ijainn.F1060.102622 (10) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics (2023) (11) Beckham, C., Pal, C.: A step towards procedural terrain generation with gans. arXiv preprint arXiv:1707.03383 (2017) (12) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics (2023) (11) Beckham, C., Pal, C.: A step towards procedural terrain generation with gans. arXiv preprint arXiv:1707.03383 (2017) (12) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Beckham, C., Pal, C.: A step towards procedural terrain generation with gans. arXiv preprint arXiv:1707.03383 (2017) (12) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). 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International journal of computer vision 115, 211–252 (2015) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. 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Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015)
  7. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. Commun. ACM 63(11), 139–144 (2020). https://doi.org/10.1145/3422622 (9) Sisodia, Y.: Gan-generated terrain for game assets. Indian Journal of Artificial Intelligence and Neural Networking 2, 1–3 (2022). https://doi.org/10.54105/ijainn.F1060.102622 (10) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics (2023) (11) Beckham, C., Pal, C.: A step towards procedural terrain generation with gans. arXiv preprint arXiv:1707.03383 (2017) (12) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Sisodia, Y.: Gan-generated terrain for game assets. Indian Journal of Artificial Intelligence and Neural Networking 2, 1–3 (2022). https://doi.org/10.54105/ijainn.F1060.102622 (10) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics (2023) (11) Beckham, C., Pal, C.: A step towards procedural terrain generation with gans. arXiv preprint arXiv:1707.03383 (2017) (12) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics (2023) (11) Beckham, C., Pal, C.: A step towards procedural terrain generation with gans. arXiv preprint arXiv:1707.03383 (2017) (12) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Beckham, C., Pal, C.: A step towards procedural terrain generation with gans. arXiv preprint arXiv:1707.03383 (2017) (12) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015)
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Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Beckham, C., Pal, C.: A step towards procedural terrain generation with gans. arXiv preprint arXiv:1707.03383 (2017) (12) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015)
  9. Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics (2023) (11) Beckham, C., Pal, C.: A step towards procedural terrain generation with gans. arXiv preprint arXiv:1707.03383 (2017) (12) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Beckham, C., Pal, C.: A step towards procedural terrain generation with gans. arXiv preprint arXiv:1707.03383 (2017) (12) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015)
  10. Beckham, C., Pal, C.: A step towards procedural terrain generation with gans. arXiv preprint arXiv:1707.03383 (2017) (12) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015)
  11. Panagiotou, E., Charou, E.: Procedural 3d terrain generation using generative adversarial networks. arXiv preprint arXiv:2010.06411 (2020) (13) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015)
  12. Huang, Y.-L., Yuan, X.-F.: Styleterrain: A novel disentangled generative model for controllable high-quality procedural terrain generation. Computers & Graphics 116, 373–382 (2023). https://doi.org/10.1016/j.cag.2023.09.002 (14) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015)
  13. Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600 (2010). Wiley Online Library (15) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015)
  14. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) (16) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015)
  15. Tangrams: GitHub - tangrams/heightmapper: interactive heightmaps from terrain data. https://github.com/tangrams/heightmapper/tree/main (17) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015)
  16. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International journal of computer vision 115, 211–252 (2015)

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