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：近年来，基于学习的布料模拟方法已开始展现其潜力。然而，在神经模拟中处理碰撞与交叉穿透问题仍是一个尚未解决的重大挑战。本文提出 \moniker{}，一种用于处理神经布料模拟中交叉穿透问题的基于学习的解决方案。与传统方法严重依赖无交叉输入不同，\moniker{} 能够鲁棒地从因碰撞遗漏、自穿透体或人工设计多层服装时的误差所引入的交叉穿透中恢复。\moniker{} 的技术核心是一种新颖的交叉轮廓损失函数，该函数惩罚相互穿透并鼓励其快速消解。我们将此交叉损失与一个避免碰撞的排斥目标集成到一个基于图神经网络（GNNs）的神经布料模拟方法中。我们在动态人体运动下的一系列具有挑战性的多样化多层服装上展示了我们方法的能力。广泛的实验分析表明，\moniker{} 显著改进了学习模拟中的碰撞处理，并产生了视觉上引人注目的结果。