MVD-Fusion: Single-view 3D via Depth-consistent Multi-view Generation

Hanzhe Hu; Zhizhuo Zhou; Varun Jampani; Shubham Tulsiani

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arXiv · 2404.03656

MVD-Fusion: Single-view 3D via Depth-consistent Multi-view Generation

Hanzhe Hu·Zhizhuo Zhou·Varun Jampani·Shubham Tulsiani

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Abstract

We present MVD-Fusion: a method for single-view 3D inference via generative modeling of multi-view-consistent RGB-D images. While recent methods pursuing 3D inference advocate learning novel-view generative models, these generations are not 3D-consistent and require a distillation process to generate a 3D output. We instead cast the task of 3D inference as directly generating mutually-consistent multiple views and build on the insight that additionally inferring depth can provide a mechanism for enforcing this consistency. Specifically, we train a denoising diffusion model to generate multi-view RGB-D images given a single RGB input image and leverage the (intermediate noisy) depth estimates to obtain reprojection-based conditioning to maintain multi-view consistency. We train our model using large-scale synthetic dataset Obajverse as well as the real-world CO3D dataset comprising of generic camera viewpoints. We demonstrate that our approach can yield more accurate synthesis compared to recent state-of-the-art, including distillation-based 3D inference and prior multi-view generation methods. We also evaluate the geometry induced by our multi-view depth prediction and find that it yields a more accurate representation than other direct 3D inference approaches.

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BibTeX RIS

Hanzhe Hu, Zhizhuo Zhou, Varun Jampani, Shubham Tulsiani. 2024-04-04. MVD-Fusion: Single-view 3D via Depth-consistent Multi-view Generation. https://arxiv.org/abs/2404.03656

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