Learning to Resolve Intersections in Neural Multi-Garment Simulations

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Learning to Resolve Intersections in Neural Multi-Garment Simulations

Teaser — We present a novel graph neural network-based approach to learned simulation of multilayered garments. Its key component is an Intersection Contour objective term that encourages resolution of existing cloth-cloth intersections. Even when initialized with intersecting meshes, our approach resolves penetrations (left), thus opening the door to learning-based simulation of detailed multi-layer garments (middle) and multi-garment outfits (right).

ContourCraft is a learning-based solution for handling intersections in neural cloth simulations. Unlike conventional approaches that critically rely on intersection-free inputs, ContourCraft robustly recovers from intersections introduced through missed collisions, self-penetrating bodies, or errors in manually designed multi-layer outfits. The technical core of ContourCraft 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 ContourCraft significantly improves collision handling for learned simulation and produces visually compelling results.