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Towards Part-Aware Monocular 3D Human Pose Estimation: An Architecture Search Approach
Chen, Zerui1,3; Huang, Yan1; Yu, Hongyuan1,3; Xue, Bin3; Han, Ke1; Guo, Yiru5; Wang, Liang1,2,4
2020-08
Conference NameEuropean Conference on Computer Vision
Conference Date2020.8.24-2020.8.28
Conference PlaceOnline
Abstract

Even though most existing monocular 3D pose estimation approaches achieve very competitive results, they ignore the heterogeneity among human body parts by estimating them with the same network architecture. To accurately estimate 3D poses of different body parts, we attempt to build a part-aware 3D pose estimator by searching a set of network architectures. Consequently, our model automatically learns to select a suitable architecture to estimate each body part. Compared to models built on the commonly used ResNet-50 backbone, it reduces 62% parameters and achieves better performance. With roughly the same computational complexity as previous models, our approach achieves state-of-the-art results on both the single-person and multi-person 3D pose estimation benchmarks.

Indexed ByEI
Sub direction classification机器学习
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/44425
Collection模式识别实验室
Corresponding AuthorChen, Zerui
Affiliation1.Center for Research on Intelligent Perception and Computing, NLPR, CASIA
2.Center for Excellence in Brain Science and Intelligence Technology, CAS
3.School of Artificial Intelligence, University of Chinese Academy of Sciences
4.Chinese Academy of Sciences, Artificial Intelligence Research (CAS-AIR)
5.School of Astronautics, Beihang University
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Chen, Zerui,Huang, Yan,Yu, Hongyuan,et al. Towards Part-Aware Monocular 3D Human Pose Estimation: An Architecture Search Approach[C],2020.
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