LUIVITON decomposes 3D virtual try-on into geometry-driven clothing-to-SMPL and diffusion-based body-to-SMPL correspondences, then registers and simulates garment draping on arbitrary humanoids.
Nicp: neural icp for 3d human registration at scale
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SGSoft introduces a template-guided pipeline that fuses semantic and geometric features to learn dense correspondences across deformable 3D shapes with claimed SOTA generalization and real-time efficiency.
citing papers explorer
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LUIVITON: Learned Universal Interoperable VIrtual Try-ON
LUIVITON decomposes 3D virtual try-on into geometry-driven clothing-to-SMPL and diffusion-based body-to-SMPL correspondences, then registers and simulates garment draping on arbitrary humanoids.
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SGSoft: Learning Fused Semantic-Geometric Features for 3D Shape Correspondence via Template-Guided Soft Signals
SGSoft introduces a template-guided pipeline that fuses semantic and geometric features to learn dense correspondences across deformable 3D shapes with claimed SOTA generalization and real-time efficiency.