Creating animatable avatars from static scans requires the modeling of clothing deformations in different poses. Existing learning-based methods typically add pose-dependent deformations upon a minimally-clothed mesh template or a learned implicit template, which have limitations in capturing details or hinder end-to-end learning. In this paper, we revisit point-based solutions and propose to decompose explicit garment-related templates and then add pose-dependent wrinkles to them. In this way, the clothing deformations are disentangled such that the pose-dependent wrinkles can be better learned and applied to unseen poses. Additionally, to tackle the seam artifact issues in recent state-of-the-art point-based methods, we propose to learn point features on a body surface, which establishes a continuous and compact feature space to capture the fine-grained and pose-dependent clothing geometry. To facilitate the research in this field, we also introduce a high-quality scan dataset of humans in real-world clothing. Our approach is validated on two existing datasets and our newly introduced dataset, showing better clothing deformation results in unseen poses. The project page with code and dataset can be found at https://zhanghongwen.cn/closet.

翻译：从静态扫描创建可动画化的人体化身需要对不同姿态下的服装变形进行建模。现有的基于学习的方法通常在最小着装网格模板或学习的隐式模板上添加姿态相关形变，这些方法在捕捉细节方面存在局限或阻碍端到端学习。本文重新审视基于点云的解决方案，提出分解显式服装相关模板并在此基础上添加姿态相关褶皱的方法。通过这种方式，服装变形被解耦，使得姿态相关褶皱能够更好地学习并应用于未见姿态。此外，针对当前先进点云方法中的接缝伪影问题，我们提出在人体表面学习点特征，从而建立连续紧凑的特征空间以捕捉细粒度的姿态相关服装几何。为促进该领域研究，我们还引入了真实世界着装人体的高质量扫描数据集。我们的方法在两个现有数据集及新引入数据集上得到验证，在未见姿态中展现出更优的服装变形效果。项目页面（含代码与数据集）详见 https://zhanghongwen.cn/closet。