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

2024

Conference Paper

ps

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 ContourCraft, 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.

@inproceedings{ContourCraft:2024,
  title = {{ContourCraft}: Learning to Resolve Intersections in Neural Multi-Garment Simulations},
  author = {Grigorev, Artur and Becherini, Giorgio and Black, Michael and Hilliges, Otmar and Thomaszewski, Bernhard},
  booktitle = {ACM SIGGRAPH 2024 Conference Papers},
  pages = {1--10},
  series = {SIGGRAPH '24},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  month = jul,
  year = {2024},
  doi = {10.1145/3641519.3657408},
  month_numeric = {7}
}