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.

翻译：基于学习的方法在布料模拟领域近年来已展现出潜力。然而，在神经模拟中处理碰撞与交叉问题仍是一个尚未解决的难题。本文提出名为\moniker{}的基于学习的解决方案，用于处理神经布料模拟中的交叉问题。不同于传统方法严重依赖无交叉初始输入，\moniker{}能够从漏检碰撞、自穿透体或人工设计的多层服装误差所引发的交叉状态中稳健恢复。其技术核心在于一种创新的交叉轮廓损失函数，该函数通过惩罚相互穿透并促进快速解析交叉问题。我们将该交叉损失与碰撞避免排斥目标相结合，集成到基于图神经网络（GNNs）的神经布料模拟方法中。通过动态人体动作下具有挑战性的多样化多层服装数据集，我们验证了该方法的有效性。广泛分析表明，\moniker{}显著提升了学习型模拟的碰撞处理能力，并生成了视觉上令人信服的模拟结果。