Web: http://arxiv.org/abs/2209.10845

Sept. 23, 2022, 1:11 a.m. | Ren Li, Benoît Guillard, Edoardo Remelli, Pascal Fua

cs.LG updates on arXiv.org arxiv.org

Existing data-driven methods for draping garments over posed human bodies,
despite being effective, cannot handle garments of arbitrary topology and are
typically not end-to-end differentiable. To address these limitations, we
propose an end-to-end differentiable pipeline that represents garments using
implicit surfaces and learns a skinning field conditioned on shape and pose
parameters of an articulated body model. To limit body-garment
interpenetrations and artifacts, we propose an interpretation-aware
pre-processing strategy of training data and a novel training loss that
penalizes self-intersections …

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