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We propose \textbf{DMV3D}, a novel 3D generation approach that uses atransformer-based 3D large reconstruction model to denoise multi-viewdiffusion. Our reconstruction model incorporates a triplane NeRF representationand 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 diverseobjects using only image reconstruction losses, without accessing 3D assets. Wedemonstrate state-of-the-art results for the single-image reconstructionproblem where probabilistic modeling of unseen object parts is required forgenerating diverse reconstructions with sharp textures. We also showhigh-quality text-to-3D generation results outperforming previous 3D diffusionmodels. Our project website is at: https://justimyhxu.github.io/projects/dmv3d/ .

DMV3D: Denoising Multi-View Diffusion using 3D Large Reconstruction Model