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arxiv:2311.09217

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

Published on Nov 15, 2023
ยท Submitted by akhaliq on Nov 16, 2023
#2 Paper of the day
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Abstract

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 can denoise noisy multi-view images via NeRF reconstruction and rendering, achieving single-stage 3D generation in sim30s on single A100 GPU. We train 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/ .

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DMV3D: Breakthrough in High-Fidelity 3D Reconstruction and Denoising

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