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://www.liuyebin.com/closet.

翻译：从静态扫描创建可动画化虚拟化身需要模拟不同姿态下的服装变形。现有基于学习方法通常基于最小化着装网格模板或习得隐式模板添加姿态相关变形，但在细节捕捉上存在局限或阻碍端到端学习。本文重新审视基于点的方法，提出分解显式服装相关模板并对其施加姿态相关褶皱。通过此方式，服装变形得以解耦，使得姿态相关褶皱能够被更好学习并适用于未见姿态。此外，针对近期基于点方法中的接缝伪影问题，我们提出在人体表面学习点特征，建立连续紧凑的特征空间以捕捉细粒度且姿态相关的服装几何结构。为促进该领域研究，我们还引入一个真实服装人体高质量扫描数据集。在两个现有数据集及我们新提出的数据集上的验证表明，本文方法在未见姿态下展现出更优的服装变形效果。项目页面（含代码与数据集）请见 https://www.liuyebin.com/closet。