ContourCraft：学习解决神经多服装模拟中的交叉问题
ContourCraft: Learning to Resolve Intersections in Neural Multi-Garment Simulations
AI总结：
ContourCraft 提出一种基于图神经网络和交叉轮廓损失的神经布料模拟方法，解决多层服装中的交叉问题，显著提升碰撞处理效果。
摘要：
基于学习的方法在布料模拟领域近年来开始展现出其潜力。然而，在神经模拟中处理碰撞和交叉仍然是一个 largely 未解决的问题。在这项工作中，我们提出了 ContourCraft，一种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。与严重依赖无交叉输入的传统方法不同，ContourCraft 能够稳健地从因遗漏碰撞、自穿透物体或手动设计的多层服装中的错误而引入的交叉中恢复。ContourCraft 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快速解决这些问题。我们将交叉损失与避免碰撞的排斥目标集成到一种基于图神经网络（GNN）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。我们的广泛分析表明，ContourCraft 显著改善了学习模拟的碰撞处理，并产生了视觉上引人注目的结果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
