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• W4386076296 abstract "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." @default.
• W4386076296 created "2023-08-23" @default.
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• W4386076296 date "2023-06-01" @default.
• W4386076296 modified "2023-10-16" @default.
• W4386076296 title "GarmentTracking: Category-Level Garment Pose Tracking" @default.
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• W4386076296 doi "https://doi.org/10.1109/cvpr52729.2023.02034" @default.
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