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.

翻译：我们提出ClothCombo管道，用于在不同体型和姿态的三维人体模型上悬垂任意衣物组合。现有基于学习的衣物悬垂方法虽展现出可喜成果，但多层衣物因衣物间交互建模的复杂性仍具挑战性。为此，本方法采用基于图神经网络（GNN）的网络高效建模不同层级衣物间的交互，从而支持多层衣物悬垂。具体而言，我们首先通过拓扑无关网络为每件衣物创建特征嵌入；接着，悬垂网络在不考虑衣物间交互的情况下对所有衣物进行变形以适配目标体型与姿态；最后，解缠网络以解决衣物间穿透问题的方式预测逐顶点位移。实验表明，所提模型在复杂多层场景中展现出强劲性能。由于本方法对衣物拓扑架构保持无关性，可便捷应用于真实衣物在多样化姿态与衣物组合下的分层虚拟试穿。