Papers
Topics
Authors
Recent
Search
2000 character limit reached

Boosting Architectural Generation via Prompts: Report

Published 24 Apr 2024 in cs.HC | (2404.15971v1)

Abstract: In the realm of AI architectural design, the importance of prompts is becoming increasingly prominent. With advancements in artificial intelligence and large-scale model technology, more design tasks are being delegated to machine learning algorithms. This necessitates a method for designers to guide algorithms in producing their desired designs. Prompts serve as a guiding and motivational mechanism, playing a crucial role in AI-generated architectural design. This paper categorizes and summarizes common vocabulary used in architectural design, discussing how to craft effective prompts and their impact on the quality and creativity of generated results. Through careful prompt design, designers can better control the generated architectural design images, thereby achieving designs that are more aligned with requirements and innovative.

Authors (2)
Definition Search Book Streamline Icon: https://streamlinehq.com
References (34)
  1. R. H. McGuire and M. B. Schiffer, “A theory of architectural design,” Journal of anthropological archaeology, vol. 2, no. 3, pp. 277–303, 1983.
  2. D. Garlan, R. Allen, and J. Ockerbloom, “Exploiting style in architectural design environments,” ACM SIGSOFT software engineering notes, vol. 19, no. 5, pp. 175–188, 1994.
  3. V. Machairas, A. Tsangrassoulis, and K. Axarli, “Algorithms for optimization of building design: A review,” Renewable and sustainable energy reviews, vol. 31, pp. 101–112, 2014.
  4. O. O. Demirbaş and H. Demirkan, “Focus on architectural design process through learning styles,” Design studies, vol. 24, no. 5, pp. 437–456, 2003.
  5. I. Caetano, L. Santos, and A. Leitão, “Computational design in architecture: Defining parametric, generative, and algorithmic design,” Frontiers of Architectural Research, vol. 9, no. 2, pp. 287–300, 2020.
  6. X. Xu, I. Weber, M. Staples, L. Zhu, J. Bosch, L. Bass, C. Pautasso, and P. Rimba, “A taxonomy of blockchain-based systems for architecture design,” in 2017 IEEE international conference on software architecture (ICSA).   IEEE, 2017, pp. 243–252.
  7. T. Kotnik, “Digital architectural design as exploration of computable functions,” International journal of architectural computing, vol. 8, no. 1, pp. 1–16, 2010.
  8. A. Hollberg and J. Ruth, “Lca in architectural design—a parametric approach,” The International Journal of Life Cycle Assessment, vol. 21, pp. 943–960, 2016.
  9. D. Aliakseyeu, J.-B. Martens, and M. Rauterberg, “A computer support tool for the early stages of architectural design,” Interacting with Computers, vol. 18, no. 4, pp. 528–555, 2006.
  10. Ö. Akin and C. Akin, “Frames of reference in architectural design: analysing the hyperacclamation (aha-!),” Design studies, vol. 17, no. 4, pp. 341–361, 1996.
  11. K. J. Lomas, “Architectural design of an advanced naturally ventilated building form,” Energy and Buildings, vol. 39, no. 2, pp. 166–181, 2007.
  12. J. W. Rae, S. Borgeaud, T. Cai, K. Millican, J. Hoffmann, F. Song, J. Aslanides, S. Henderson, R. Ring, S. Young et al., “Scaling language models: Methods, analysis & insights from training gopher,” arXiv preprint arXiv:2112.11446, 2021.
  13. J. Xu, S. D. Mello, S. Liu, W. Byeon, T. Breuel, J. Kautz, and X. Wang, “Groupvit: Semantic segmentation emerges from text supervision,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 18 113–18 123, 2022.
  14. P. Li, Y. Ding, L. Li, J. Guan, and Z. Li, “Towards practical consistent video depth estimation,” in Proceedings of the 2023 ACM International Conference on Multimedia Retrieval, 2023, pp. 388–397.
  15. R. Thoppilan, D. De Freitas, J. Hall, N. Shazeer, A. Kulshreshtha, H.-T. Cheng, A. Jin, T. Bos, L. Baker, Y. Du et al., “LaMDA: Language models for dialog applications,” arXiv preprint arXiv:2201.08239, 2022.
  16. T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al., “Language models are few-shot learners,” in Proceedings of the 34th International Conference on Neural Information Processing Systems, 2020, pp. 1877–1901.
  17. H. Liu, D. Tam, M. Mohammed, J. Mohta, T. Huang, M. Bansal, and C. Raffel, “Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning,” in Proceedings of the 36th International Conference on Neural Information Processing Systems, 2022.
  18. R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 10 684–10 695.
  19. OpenAI, “Gpt-4 technical report,” 2023.
  20. A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al., “Learning transferable visual models from natural language supervision,” in Proceedings of the International Conference on Machine Learning, 2021, pp. 8748–8763.
  21. F.-A. Croitoru, V. Hondru, R. T. Ionescu, and M. Shah, “Diffusion models in vision: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023.
  22. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778.
  23. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems, vol. 30, 2017.
  24. K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in Proceedings of the IEEE international conference on computer vision, 2017, pp. 2961–2969.
  25. P. Li, B. Li, and Z. Li, “Sketch-to-architecture: Generative ai-aided architectural design,” in Proceedings of the 31st Pacific Conference on Computer Graphics and Applications.   The Eurographics Association, 2023.
  26. N. Nauata, K.-H. Chang, C.-Y. Cheng, G. Mori, and Y. Furukawa, “House-gan: Relational generative adversarial networks for graph-constrained house layout generation,” in Proceedings of the European Conference on Computer Vision, 2020.
  27. P. Li and B. Li, “Generating daylight-driven architectural design via diffusion models,” arXiv preprint arXiv:2404.13353, 2024.
  28. S. Chaillou, “Archigan: Artificial intelligence x architecture,” in Architectural intelligence: Selected papers from the 1st international conference on computational design and robotic fabrication (CDRF 2019).   Springer, 2020, pp. 117–127.
  29. W. R. Para, S. Bhat, P. Guerrero, T. Kelly, N. J. Mitra, L. J. Guibas, and P. Wonka, “Sketchgen: Generating constrained cad sketches,” in Proceedings of the 35th International Conference on Neural Information Processing Systems, 2021.
  30. M. Jia, L. Tang, B.-C. Chen, C. Cardie, S. Belongie, B. Hariharan, and S.-N. Lim, “Visual prompt tuning,” in European Conference on Computer Vision.   Springer, 2022, pp. 709–727.
  31. K. Zhou, J. Yang, C. C. Loy, and Z. Liu, “Learning to prompt for vision-language models,” International Journal of Computer Vision, vol. 130, no. 9, pp. 2337–2348, 2022.
  32. V. Liu and L. B. Chilton, “Design guidelines for prompt engineering text-to-image generative models,” in Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, 2022, pp. 1–23.
  33. D. Li, J. Li, H. Li, J. C. Niebles, and S. C. Hoi, “Align and prompt: Video-and-language pre-training with entity prompts,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 4953–4963.
  34. Y. Hao, Z. Chi, L. Dong, and F. Wei, “Optimizing prompts for text-to-image generation,” Advances in Neural Information Processing Systems, vol. 36, 2024.
Citations (1)

Summary

No one has generated a summary of this paper yet.

Paper to Video (Beta)

No one has generated a video about this paper yet.

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Continue Learning

We haven't generated follow-up questions for this paper yet.