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Score Jacobian Chaining: Lifting Pretrained 2D Diffusion Models for 3D Generation

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arxiv 2212.00774 v1 pith:G5M6RFBF submitted 2022-12-01 cs.CV cs.LG

Haochen Wang , Xiaodan Du , Jiahao Li , Raymond A. Yeh , Greg Shakhnarovich This is my paper · ORCID
classification cs.CVcs.LG
keywords diffusionmodelscorefieldgenerationgradientsjacobianmodels
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A diffusion model learns to predict a vector field of gradients. We propose to apply chain rule on the learned gradients, and back-propagate the score of a diffusion model through the Jacobian of a differentiable renderer, which we instantiate to be a voxel radiance field. This setup aggregates 2D scores at multiple camera viewpoints into a 3D score, and repurposes a pretrained 2D model for 3D data generation. We identify a technical challenge of distribution mismatch that arises in this application, and propose a novel estimation mechanism to resolve it. We run our algorithm on several off-the-shelf diffusion image generative models, including the recently released Stable Diffusion trained on the large-scale LAION dataset.

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Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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  5. SHaDe: Compact and Consistent Dynamic 3D Reconstruction via Tri-Plane Deformation and Latent Diffusion

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    A feed-forward system that generates object-level and scene-level 3D Gaussian scenes from text in about eight seconds by diffusing multi-view RGB-D latent codes and decoding them into pixel-aligned 3D Gaussians.

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