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

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

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publicationAdvances in Computer Graphics - 37th Computer Graphics International Conference, CGI 2020, Proceedings
EditorsNadia Magnenat-Thalmann, Constantine Stephanidis, George Papagiannakis, Enhua Wu, Daniel Thalmann, Bin Sheng, Jinman Kim, Marina Gavrilova
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages8
ISBN (Print)9783030618636
Publication statusPublished - Oct 2020
Event37th Computer Graphics International Conference, CGI 2020 - Geneva, Switzerland
Duration: 20 Oct 202023 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12221 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference37th Computer Graphics International Conference, CGI 2020


  • 3D hand pose estimation
  • Adaptive graph convolution
  • Depth image

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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