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[bibtex]@InProceedings{Wu_2023_WACV, author = {Wu, Erwin and Nishioka, Hayato and Furuya, Shinichi and Koike, Hideki}, title = {Marker-Removal Networks To Collect Precise 3D Hand Data for RGB-Based Estimation and Its Application in Piano}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2023}, pages = {2977-2986} }
Marker-Removal Networks To Collect Precise 3D Hand Data for RGB-Based Estimation and Its Application in Piano
Abstract
Hand pose analysis is a key step to understanding dexterous hand performances of many high-level skills, such as playing the piano. Currently, most accurate hand tracking systems are using fabric-/marker-based sensing that potentially disturbs users' performance. On the other hand, markerless computer vision-based methods rely on a precise bare-hand dataset for training, which is difficult to obtain. In this paper, we collect a large-scale high precision 3D hand pose dataset with a small workload using a novel marker-removal network (MR-Net). The proposed MR-Net translates the marked-hand images to realistic bare-hand images, and the corresponding 3D postures are captured by a motion capture system thus few manual annotations are required. A baseline estimation network PiaNet is introduced and we report the accuracy of various metrics together with a blind qualitative test to show the practical effect.
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