GHand: A Graph Convolution Network for 3D Hand Pose Estimation

Pengsheng Wang, Guangtao Xue, Ping Li, Jinman Kim, Bin Sheng, Lijuan Mao

Abstract: Vision-based 3D hand pose estimation plays an important role in the field of human-computer interaction. In recent years, with the development of convolutional neural networks (CNN), the field of 3D hand pose estimation has made a great progress, but there is still a long way to go before the problem is solved. Although recent studies based on CNN networks have greatly improved the recognition accuracy, they usually only pay attention on the regression ability of the network itself, and ignore the structural information of the hands, thus leads to a low accuracy in contrast. In this paper we proposed a new hand pose estimation network, which can fully learn the structural information of hands through an adaptive graph convolutional neural network. The experiment on the public dataset shows the accuracy of our graph convolution network exceeds the SOTA methods in 3D hand pose estimation.