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.

通过将预训练的二维扩散模型引入神经光辐射场（NeRFs），文本到三维生成方法取得了巨大的进展，其中许多最先进的方法通常使用得分蒸馏采样（SDS）来优化 NeRF 表示，该方法通过预训练的文本条件的二维扩散模型（例如 ImData）监督 NeRF 优化。然而，由这种预训练扩散模型提供的监督信号仅依赖于文本提示，并不限制多视角一致性。为了将跨视角一致性引入扩散先验中，一些最近的工作通过多视角数据微调二维扩散模型，但仍缺乏细粒度的视图连贯性。为了解决这个挑战，我们将多视角图像条件纳入 NeRF 优化的监督信号中，明确强制执行细粒度的视图一致性。通过这种更强的监督，我们提出的文本到三维方法有效地减轻了由于过高密度而产生的浮动点和由于密度不足而形成的完全空白空间。我们在 T$^3$Bench 数据集上的定量评估表明，我们的方法在现有的文本到三维方法中达到了最先进的性能。我们将公开发布代码。