Garments are important to humans. A visual system that can estimate and track the complete garment pose can be useful for many downstream tasks and real-world applications. In this work, we present a complete package to address the category-level garment pose tracking task: (1) A recording system VR-Garment, with which users can manipulate virtual garment models in simulation through a VR interface. (2) A large-scale dataset VR-Folding, with complex garment pose configurations in manipulation like flattening and folding. (3) An end-to-end online tracking framework GarmentTracking, which predicts complete garment pose both in canonical space and task space given a point cloud sequence. Extensive experiments demonstrate that the proposed GarmentTracking achieves great performance even when the garment has large non-rigid deformation. It outperforms the baseline approach on both speed and accuracy. We hope our proposed solution can serve as a platform for future research. Codes and datasets are available in https://garment-tracking.robotflow.ai.
@article{arxiv.2303.13913,
title = {GarmentTracking: Category-Level Garment Pose Tracking},
author = {Han Xue and Wenqiang Xu and Jieyi Zhang and Tutian Tang and Yutong Li and Wenxin Du and Ruolin Ye and Cewu Lu},
journal= {arXiv preprint arXiv:2303.13913},
year = {2025}
}