Text-Image Conditioned Diffusion for Consistent Text-to-3D Generation

• URL: http://arxiv.org/abs/2312.11774v1
• Date: Tue, 19 Dec 2023 01:09:49 GMT
• Title: Text-Image Conditioned Diffusion for Consistent Text-to-3D Generation
• Authors: Yuze He, Yushi Bai, Matthieu Lin, Jenny Sheng, Yubin Hu, Qi Wang, Yu-Hui Wen, Yong-Jin Liu
• Abstract summary: We propose a text-to-3D method for Neural Radiance Fields (NeRFs) that explicitly enforces fine-grained view consistency. Our method achieves state-of-the-art performance over existing text-to-3D methods.
• Score: 28.079441901818296
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: By lifting the pre-trained 2D diffusion models into Neural Radiance Fields (NeRFs), text-to-3D generation methods have made great progress. Many state-of-the-art approaches usually apply score distillation sampling (SDS) to optimize the NeRF representations, which supervises the NeRF optimization with pre-trained text-conditioned 2D diffusion models such as Imagen. However, the supervision signal provided by such pre-trained diffusion models only depends on text prompts and does not constrain the multi-view consistency. To inject the cross-view consistency into diffusion priors, some recent works finetune the 2D diffusion model with multi-view data, but still lack fine-grained view coherence. To tackle this challenge, we incorporate multi-view image conditions into the supervision signal of NeRF optimization, which explicitly enforces fine-grained view consistency. With such stronger supervision, our proposed text-to-3D method effectively mitigates the generation of floaters (due to excessive densities) and completely empty spaces (due to insufficient densities). Our quantitative evaluations on the T$^3$Bench dataset demonstrate that our method achieves state-of-the-art performance over existing text-to-3D methods. We will make the code publicly available.

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