LUIVITON: 学习通用互操作虚拟试衣
LUIVITON: Learned Universal Interoperable VIrtual Try-ON
AI总结：
本文提出一种全自动虚拟试衣系统，通过SMPL作为中间代理，解决服装与人体之间的对应问题，实现复杂多层服装在多样化人体上的合理 draped 效果，且支持快速定制。
摘要：
为实现大规模重用真实世界3D资产，我们提出一个全自动虚拟试衣系统，能够将复杂多层服装穿在多样化的人形上。我们的核心思想是利用SMPL作为中间代理，将服装到人体的转换分解为两个具有不同挑战的对应任务：(1) 服装到SMPL（部分到完整对齐）和(2) 人体到SMPL（大姿态/形状变化和风格化）。我们使用几何驱动的对应模型解决服装到SMPL的问题，并引入基于扩散的身体到SMPL对应方法，结合多视角一致的外观特征和预训练的2D基础模型。利用这些对应关系，我们将SMPL/SMPL+D（位移）注册到服装和目标人体，并通过转移服装沿平滑的SMPL到SMPL+D过渡进行模拟驱动的拟合，产生物理上合理的 draped 效果。我们的系统处理复杂服装拓扑（包括非流形网格）并泛化到广泛的人形角色（例如人类、机器人、卡通和生物）的同时保持计算实用性。在 draped 之后，我们的系统还支持快速定制服装尺寸。我们展示我们的系统可以在没有人工劳动的情况下产生高质量的3D服装拟合，即使没有2D服装缝纫图案。我们的项目页面是：https://cao-cong0.github.io/LUIVITON-Learned-Universal-Interoperable-VIrtual-Try-ON/.
英文摘要：
To enable large-scale reuse of real-world 3D assets, where garments and characters rarely share skeletons, templates, or dense correspondences, we present a fully automated virtual try-on system that dresses complex, multi-layer garments onto diverse, arbitrarily posed humanoids. Our key idea is to use SMPL as an intermediate proxy and decompose clothing-to-body transfer into two correspondence tasks with distinct challenges: (1) clothing-to-SMPL (partial-to-complete alignment) and (2) body-to-SMPL (large pose/shape variation and stylization). We address clothing-to-SMPL using a geometry-driven correspondence model, and introduce a diffusion-based body-to-SMPL correspondence approach that leverages multi-view consistent appearance features together with a pretrained 2D foundation model. Using these correspondences, we register SMPL/SMPL+D (Displacement) to the garment and target body and then perform simulator-driven fitting by transferring the garment along a smooth SMPL-to-SMPL+D transition, producing physically plausible draping on the target. Our system handles complex garment topology (including non-manifold meshes) and generalizes to a wide range of humanoid characters (e.g., humans, robots, cartoons, and creatures) while remaining computationally practical. Upon draping, our system also supports fast customization of clothing size. We show that our system can produce high-quality 3D clothing fittings without any human labor, even when 2D clothing sewing patterns are not available. Our project page is: https://cao-cong0.github.io/LUIVITON-Learned-Universal-Interoperable-VIrtual-Try-ON/.
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。
