  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.
