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 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 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.