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 T3Bench dataset demonstrate
that our method achieves state-of-the-art performance over existing text-to-3D
methods. We will make the code publicly available