ContourCraft: Learning to Resolve Intersections in Neural Multi-Garment Simulations
<|reference_start|>ContourCraft: Learning to Resolve Intersections in Neural Multi-Garment Simulations: 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.<|reference_end|>
@article{grigorev2024contourcraft:,
    title={ContourCraft: Learning to Resolve Intersections in Neural Multi-Garment
  Simulations},
    author={Artur Grigorev, Giorgio Becherini, Michael J. Black, Otmar Hilliges,
  Bernhard Thomaszewski},
    journal={arXiv preprint arXiv:2405.09522},
    year={2024},
    doi={10.1145/3641519.3657408},
    archivePrefix={arXiv},
    eprint={2405.09522},
    primaryClass={cs.GR cs.LG}
}