Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures (2211.07600v1)

Abstract: Text-guided image generation has progressed rapidly in recent years, inspiring major breakthroughs in text-guided shape generation. Recently, it has been shown that using score distillation, one can successfully text-guide a NeRF model to generate a 3D object. We adapt the score distillation to the publicly available, and computationally efficient, Latent Diffusion Models, which apply the entire diffusion process in a compact latent space of a pretrained autoencoder. As NeRFs operate in image space, a naive solution for guiding them with latent score distillation would require encoding to the latent space at each guidance step. Instead, we propose to bring the NeRF to the latent space, resulting in a Latent-NeRF. Analyzing our Latent-NeRF, we show that while Text-to-3D models can generate impressive results, they are inherently unconstrained and may lack the ability to guide or enforce a specific 3D structure. To assist and direct the 3D generation, we propose to guide our Latent-NeRF using a Sketch-Shape: an abstract geometry that defines the coarse structure of the desired object. Then, we present means to integrate such a constraint directly into a Latent-NeRF. This unique combination of text and shape guidance allows for increased control over the generation process. We also show that latent score distillation can be successfully applied directly on 3D meshes. This allows for generating high-quality textures on a given geometry. Our experiments validate the power of our different forms of guidance and the efficiency of using latent rendering. Implementation is available at https://github.com/eladrich/latent-nerf

• The paper introduces latent score distillation within a compact latent space to efficiently guide Neural Radiance Fields for 3D shape synthesis.
• It employs Sketch-Shape guidance to direct geometric control, aligning abstract prompts with specific shape structures.
• The method delivers improved multi-view fidelity and precise texture mapping, outperforming previous approaches like DreamFields and CLIPMesh.

Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures

The paper "Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures" introduces an innovative approach to 3D shape and texture generation by leveraging latent diffusion models in conjunction with Neural Radiance Fields (NeRFs). This research explores the intersection of 3D shape synthesis with latent representations, providing novel insights into controlled shape generation.

Summary

The authors present a method to generate 3D shapes by adapting score distillation for latent diffusion models, making use of efficient latent spaces produced by a pretrained autoencoder. This technique leads to the development of Latent-NeRF, an approach that operates in the compact latent space rather than the traditional RGB space used in conventional NeRFs.

Key Contributions

• Latent Score Distillation: By using score distillation within a latent space, this method enables the guidance of NeRFs by latent diffusion models. This approach reduces computational overhead compared to traditional RGB-based NeRF guidance.
• Shape Constrained Generation: The introduction of Sketch-Shape guidance allows for abstract geometries to direct the NeRF towards desired shapes, imposing a form of structural control that aligns with textual prompts.
• Latent-Paint for Direct Texture Mapping: This method permits the generation of textures directly onto predefined 3D meshes by optimizing within the latent space, enhancing the precision in texture application without extensive manual mapping.

Results and Comparisons

The paper showcases results demonstrating the efficacy of Latent-NeRF in generating complex 3D shapes with consistent multi-view fidelity. Comparisons indicate marked improvements over previous methods such as DreamFields and CLIPMesh, attributed to the utilization of stable diffusion models and latent space rendering.

Implications and Future Directions

Practically, Latent-NeRF offers a more efficient methodology for 3D object generation, aligning with recent advances in language-image models and diffusion models. Theoretically, it opens new avenues in the use of latent spaces for rendering purposes, suggesting a paradigm shift towards more compact and effective generative models.

Future research could explore extending this method in various domains, including virtual reality and complex scene generation. Additionally, improving the method's robustness across more challenging prompts and enhancing the refinement processes further could provide significant advancements.

Conclusion

The paper contributes significantly to the field of AI by integrating latent diffusion models with NeRFs, showcasing a practical and efficient approach to 3D shape and texture generation. This research sets the stage for further developments in latent space rendering and shape-guided generation, offering a compelling alternative to traditional 3D modeling techniques.