Skip to yearly menu bar Skip to main content

Spotlight Poster

DMV3D: Denoising Multi-view Diffusion Using 3D Large Reconstruction Model

Yinghao Xu · Hao Tan · Fujun Luan · Sai Bi · Peng Wang · Jiahao Li · Zifan Shi · Kalyan Sunkavalli · Gordon Wetzstein · Zexiang Xu · Kai Zhang

Halle B #15
[ ] [ Project Page ]
Wed 8 May 7:30 a.m. PDT — 9:30 a.m. PDT


We propose 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, functioning as a denoiser, can denoise noisy multi-view images via 3D NeRF reconstruction and rendering, achieving single-stage 3D generation in the 2D diffusion denoising process. We train DMV3D on large-scale multi-view image datasets of extremely 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:

Chat is not available.