ClothCombo：面向多层衣物悬垂的衣物间交互建模
ClothCombo: Modeling Inter-Cloth Interaction for Draping Multi-Layered Clothes
AI总结：
ClothCombo 提出基于 GNN 的流程，通过特征嵌入、悬垂变形与解缠网络建模多层衣物间交互，实现任意组合衣物在多样人体上的分层虚拟试穿。
摘要：
我们提出 ClothCombo，一个能够在具有不同体型和姿态的 3D 人体模型上对任意衣物组合进行悬垂的流程。尽管现有的基于学习的衣物悬垂方法已展现出有前景的结果，但多层衣物仍然具有挑战性，因为对衣物间交互进行建模并非易事。为此，我们的方法利用基于 GNN 的网络来高效建模不同层衣物之间的交互，从而实现多层衣物。具体来说，我们首先使用拓扑无关网络为每件衣物创建特征嵌入。然后，悬垂网络在不考虑衣物间交互的情况下使所有衣物变形以适配目标体型和姿态。最后，解缠网络预测逐顶点位移，以解决衣物之间的相互穿透问题。在实验中，所提出的模型在复杂的多层场景中表现出强大的性能。由于对衣物拓扑不敏感，我们的方法可以方便地用于真实衣物在不同姿态和衣物组合下的分层虚拟试穿。
英文摘要：
We present ClothCombo, a pipeline to drape arbitrary combinations of clothes on 3D human models with varying body shapes and poses. While existing learning-based approaches for draping clothes have shown promising results, multi-layered clothing remains challenging as it is non-trivial to model inter-cloth interaction. To this end, our method utilizes a GNN-based network to efficiently model the interaction between clothes in different layers, thus enabling multi-layered clothing. Specifically, we first create feature embedding for each cloth using a topology-agnostic network. Then, the draping network deforms all clothes to fit the target body shape and pose without considering inter-cloth interaction. Lastly, the untangling network predicts the per-vertex displacements in a way that resolves interpenetration between clothes. In experiments, the proposed model demonstrates strong performance in complex multi-layered scenarios. Being agnostic to cloth topology, our method can be readily used for layered virtual try-on of real clothes in diverse poses and combinations of clothes.
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。
