While recent AI-based draping networks have significantly advanced the ability to simulate the appearance of clothes worn by 3D human models, the handling of multi-layered garments remains a challenging task. This paper presents a model for draping multi-layered garments that are unseen during the training process. Our proposed framework consists of three stages: garment embedding, single-layered garment draping, and untangling. The model represents a garment independent to its topological structure by mapping it onto the $UV$ map of a human body model, allowing for the ability to handle previously unseen garments. In the single-layered garment draping phase, the model sequentially drapes all garments in each layer on the body without considering interactions between them. The untangling phase utilizes a GNN-based network to model the interaction between the garments of different layers, enabling the simulation of complex multi-layered clothing. The proposed model demonstrates strong performance on both unseen synthetic and real garment reconstruction data on a diverse range of human body shapes and poses.
翻译:最近基于人工智能的服装模拟网络已经显着提高了对着3D人体模型穿着的服装外观模拟的能力,但是处理多层服装仍然是一个具有挑战性的任务。本文提出了一种模型,用于模拟训练过程中未见过的多层服装的展示。我们提出的框架由三个阶段组成:服装嵌入,单层服装展示和解缠。该模型将服装独立于其拓扑结构,通过将其映射到人体模型的 UV 贴图上来实现,从而能够处理以前未见过的服装。在单层服装展示阶段,模型依次将每层的所有服装展示在身体上,而不考虑它们之间的相互作用。解缠阶段利用基于 GNN 的网络来模拟不同层次的服装之间的相互作用,从而实现复杂多层次服装的模拟。提出的模型在不同人体形状和姿势下的未见合成和真实服装重建数据上展现出强大的性能。