CASIA OpenIR  > 智能感知与计算
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
会议名称European Conference on Computer Vision
会议日期2020.8.24-2020.8.28
会议地点Online
摘要

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.

收录类别EI
七大方向——子方向分类机器学习
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/44425
专题智能感知与计算
通讯作者Chen, Zerui
作者单位1.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
第一作者单位模式识别国家重点实验室
通讯作者单位模式识别国家重点实验室
推荐引用方式
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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