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Creating animatable avatars from static scans requires the modeling ofclothing deformations in different poses. Existing learning-based methodstypically add pose-dependent deformations upon a minimally-clothed meshtemplate or a learned implicit template, which have limitations in capturingdetails or hinder end-to-end learning. In this paper, we revisit point-basedsolutions and propose to decompose explicit garment-related templates and thenadd pose-dependent wrinkles to them. In this way, the clothing deformations aredisentangled such that the pose-dependent wrinkles can be better learned andapplied to unseen poses. Additionally, to tackle the seam artifact issues inrecent state-of-the-art point-based methods, we propose to learn point featureson a body surface, which establishes a continuous and compact feature space tocapture the fine-grained and pose-dependent clothing geometry. To facilitatethe research in this field, we also introduce a high-quality scan dataset ofhumans in real-world clothing. Our approach is validated on two existingdatasets and our newly introduced dataset, showing better clothing deformationresults in unseen poses. The project page with code and dataset can be found athttps://www.liuyebin.com/closet.

CloSET: Modeling Clothed Humans on Continuous Surface with Explicit Template Decomposition