We propose \textbf{DMV3D}, a novel 3D generation approach that uses a
transformer-based 3D large reconstruction model to denoise multi-view
diffusion. Our reconstruction model incorporates a triplane NeRF representation
and can denoise noisy multi-view images via NeRF reconstruction and rendering,
achieving single-stage 3D generation in $\sim$30s on single A100 GPU. We train
\textbf{DMV3D} on large-scale multi-view image datasets of highly diverse
objects using only image reconstruction losses, without accessing 3D assets. We
demonstrate state-of-the-art results for the single-image reconstruction
problem where probabilistic modeling of unseen object parts is required for
generating diverse reconstructions with sharp textures. We also show
high-quality text-to-3D generation results outperforming previous 3D diffusion
models. Our project website is at: this https URL .

我们提出了一种新颖的 3D 生成方法 DMV3D，该方法使用基于变压器的 3D 大型重建模型对多视差扩散进行去噪。我们的重建模型采用了三平面 NeRF 表示，并且可以通过 NeRF 重建和渲染对嘈杂的多视差图像进行去噪，能在单个 A100 GPU 上实现约 30 秒的单阶段 3D 生成。我们使用大规模多视差图像数据集训练 DMV3D，仅使用图像重建损失，而不访问 3D 资产。我们展示了在需要对未见过的物体部分进行概率建模以生成具有清晰纹理的多样重建的单图像重建问题上的最新成果，以及优于以往的 3D 扩散模型的高质量文本到 3D 生成结果。我们的项目网站位于此 https URL。