GARNet: Graph Attention Residual Networks Based on Adversarial Learning for 3D Human Pose Estimation

Zhihua Chen, Xiaoli Liu, Bing Sheng, Ping Li

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

Abstract

Recent studies have shown that, with the help of complex network architecture, great progress has been made in estimating the pose and shape of a 3D human from a single image. However, existing methods fail to produce accurate and natural results for different environments. In this paper, we proposed a novel adversarial learning approach and studied the problem of learning graph attention network for regression. Graph Attention Residual Networks (GARNet), which processes regression tasks with graphic-structured data, learns to capture semantic information, such as local and global node relationships, through end-to-end training without additional supervision. The adversarial learning module is implemented by a novel multi-source discriminator network to learn the mapping from 2D pose distribution to 3D pose distribution. We conducted a comprehensive study to verify the effectiveness of our method. Experiments show that the performance of our method is superior to that of most existing techniques.

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
Pages276-287
Number of pages12
ISBN (Print)9783030618636
DOIs
Publication statusPublished - 2020
Externally publishedYes
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

Conference

Conference37th Computer Graphics International Conference, CGI 2020
Country/TerritorySwitzerland
CityGeneva
Period20/10/2023/10/20

Keywords

  • Adversarial learning
  • Graph attention networks
  • Pose estimation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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