TY - GEN
T1 - GARNet: Graph Attention Residual Networks Based on Adversarial Learning for 3D Human Pose Estimation
AU - Chen, Zhihua
AU - Liu, Xiaoli
AU - Sheng, Bing
AU - Li, Ping
N1 - Funding Information:
Supported by the National Natural Science Foundation of China (Grant No. 61672228, 61370174) and Shanghai Automotive Industry Science and Technology Development Foundation (No. 1837).
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Adversarial learning
KW - Graph attention networks
KW - Pose estimation
UR - http://www.scopus.com/inward/record.url?scp=85096609782&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-61864-3_24
DO - 10.1007/978-3-030-61864-3_24
M3 - Conference article published in proceeding or book
AN - SCOPUS:85096609782
SN - 9783030618636
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 276
EP - 287
BT - Advances in Computer Graphics - 37th Computer Graphics International Conference, CGI 2020, Proceedings
A2 - Magnenat-Thalmann, Nadia
A2 - Stephanidis, Constantine
A2 - Papagiannakis, George
A2 - Wu, Enhua
A2 - Thalmann, Daniel
A2 - Sheng, Bin
A2 - Kim, Jinman
A2 - Gavrilova, Marina
PB - Springer Science and Business Media Deutschland GmbH
T2 - 37th Computer Graphics International Conference, CGI 2020
Y2 - 20 October 2020 through 23 October 2020
ER -