SNUG: Self-Supervised Neural Dynamic Garments

• URL: http://arxiv.org/abs/2204.02219v1
• Date: Tue, 5 Apr 2022 13:50:21 GMT
• Title: SNUG: Self-Supervised Neural Dynamic Garments
• Authors: Igor Santesteban and Miguel A. Otaduy and Dan Casas
• Abstract summary: We present a self-supervised method to learn dynamic 3D deformations of garments worn by parametric human bodies. This allows us to learn models for interactive garments, including dynamic deformations and fine wrinkles, with two orders of magnitude speed up in training time.
• Score: 14.83072352654608
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present a self-supervised method to learn dynamic 3D deformations of garments worn by parametric human bodies. State-of-the-art data-driven approaches to model 3D garment deformations are trained using supervised strategies that require large datasets, usually obtained by expensive physics-based simulation methods or professional multi-camera capture setups. In contrast, we propose a new training scheme that removes the need for ground-truth samples, enabling self-supervised training of dynamic 3D garment deformations. Our key contribution is to realize that physics-based deformation models, traditionally solved in a frame-by-frame basis by implicit integrators, can be recasted as an optimization problem. We leverage such optimization-based scheme to formulate a set of physics-based loss terms that can be used to train neural networks without precomputing ground-truth data. This allows us to learn models for interactive garments, including dynamic deformations and fine wrinkles, with two orders of magnitude speed up in training time compared to state-of-the-art supervised methods

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