  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: https://justimyhxu.github.io/projects/dmv3d/ .
