Shypd.aiThe largest open index of AI toolsSJC (Score Jacobian Chaining) is an open-source AI tool that lifts pretrained 2D diffusion models for 3D generation. It applies chain rule on learned gradients to back-propagate scores through a differentiable renderer.
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SJC (Score Jacobian Chaining) is an open-source research project that enables the generation of 3D models from pretrained 2D diffusion models. Presented at CVPR 2023, this tool leverages the chain rule on learned gradients and back-propagates the score of a diffusion model through the Jacobian of a differentiable renderer, specifically a voxel radiance field. This innovative approach aggregates 2D scores from multiple camera viewpoints into a cohesive 3D score, effectively repurposing existing 2D models for 3D data generation. The project addresses the technical challenge of distribution mismatch with a novel estimation mechanism and supports off-the-shelf diffusion image generative models, including Stable Diffusion. It also includes implementations of the Karras sampler and a customized voxel NeRF.
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