BODIESReg automatically registers 3D body scans to SMPL-family models using pose-aligned initialization, achieving 82.9% success on CHI3D and 100% on MorphoMotion with mean surface-fit error below 10mm.
Weakly Supervised Deep Functional Map for Shape Matching
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abstract
A variety of deep functional maps have been proposed recently, from fully supervised to totally unsupervised, with a range of loss functions as well as different regularization terms. However, it is still not clear what are minimum ingredients of a deep functional map pipeline and whether such ingredients unify or generalize all recent work on deep functional maps. We show empirically minimum components for obtaining state of the art results with different loss functions, supervised as well as unsupervised. Furthermore, we propose a novel framework designed for both full-to-full as well as partial to full shape matching that achieves state of the art results on several benchmark datasets outperforming even the fully supervised methods by a significant margin. Our code is publicly available at https://github.com/Not-IITian/Weakly-supervised-Functional-map
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2026 1verdicts
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BODIESReg: An Open-Source Pipeline for Registering 3D Body Scans Using Pose-Aligned Initialization
BODIESReg automatically registers 3D body scans to SMPL-family models using pose-aligned initialization, achieving 82.9% success on CHI3D and 100% on MorphoMotion with mean surface-fit error below 10mm.