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Diffusion 3D Features (Diff3F): Decorating Untextured Shapes with Distilled Semantic Features [CVPR 2024]
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2026-02-20 09:29:16
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Diffusion 3D Features (Diff3F) Diffusion 3D Features (Diff3F) Decorating Untextured Shapes with Distilled Semantic Features [CVPR 2024] Niladri Shekhar Dutt 1, 2 , Sanjeev Muralikrishnan 1 , Niloy J. Mitra 1, 3 1 University College London , 2 Ready Player Me , 3 Adobe Research Arxiv Paper Code Video Poster Diff3F is a a novel feature distiller that harnesses the expressive power of in-painting diffusion features and distills them to points on 3D surfaces. Here, the proposed features are employed for point-to-point shape correspondence between assets varying in shape, pose, species and topology. We achieve this without any fine-tuning of the underlying diffusion models, and demonstrate results on untextured meshes, point clouds, and raw scans. Note that we show raw point-to-point correspondence, without any regularization or smoothing. Inputs are point clouds, non-manifold meshes, or 2-manifold meshes . The left most mesh is ...
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