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2020 | OriginalPaper | Chapter

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

Authors : Zhihua Chen, Xiaoli Liu, Bing Sheng, Ping Li

Published in: Advances in Computer Graphics

Publisher: Springer International Publishing

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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.

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Title: GARNet: Graph Attention Residual Networks Based on Adversarial Learning for 3D Human Pose Estimation
Authors: Zhihua Chen
Xiaoli Liu
Bing Sheng
Ping Li
Publisher: Springer International Publishing
Book: Advances in Computer Graphics
Print ISBN: 978-3-030-61863-6

Electronic ISBN: 978-3-030-61864-3

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-61864-3_24

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