文献类型：期刊文献

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

作者：Chen, Zhihua[1]; Liu, Xiaoli[1]; Sheng, Bing[2]; Li, Ping[3]

机构：[1] Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China; [2] Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; [3] Faculty of Information Technology, Macau University of Science and Technology, Macau, 999078, China

年份：2020

卷号：12221 LNCS

起止页码：276

外文期刊名：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

收录：EI(收录号：20204809544928)

语种：英文

外文关键词：Complex networks - Semantics - Garnets

摘要：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. ? 2020, Springer Nature Switzerland AG.