ContourCraft: Learning to Resolve Intersections in Neural Multi-Garment Simulations

• URL: http://arxiv.org/abs/2405.09522v2
• Date: Fri, 24 May 2024 11:51:32 GMT
• Title: ContourCraft: Learning to Resolve Intersections in Neural Multi-Garment Simulations
• Authors: Artur Grigorev, Giorgio Becherini, Michael J. Black, Otmar Hilliges, Bernhard Thomaszewski,
• Abstract summary: We present moniker, a learning-based solution for handling intersections in neural cloth simulations. moniker robustly recovers from intersections introduced through missed collisions, self-penetrating bodies, or errors in manually designed multi-layer outfits.
• Score: 70.38866232749886
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Learning-based approaches to cloth simulation have started to show their potential in recent years. However, handling collisions and intersections in neural simulations remains a largely unsolved problem. In this work, we present \moniker{}, a learning-based solution for handling intersections in neural cloth simulations. Unlike conventional approaches that critically rely on intersection-free inputs, \moniker{} robustly recovers from intersections introduced through missed collisions, self-penetrating bodies, or errors in manually designed multi-layer outfits. The technical core of \moniker{} is a novel intersection contour loss that penalizes interpenetrations and encourages rapid resolution thereof. We integrate our intersection loss with a collision-avoiding repulsion objective into a neural cloth simulation method based on graph neural networks (GNNs). We demonstrate our method's ability across a challenging set of diverse multi-layer outfits under dynamic human motions. Our extensive analysis indicates that \moniker{} significantly improves collision handling for learned simulation and produces visually compelling results.

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