Learning a Robust Part-Aware Monocular 3D Human Pose Estimator via Neural Architecture Search

doi:10.1007/s11263-021-01525-0

CORC > 自动化研究所 > 中国科学院自动化研究所 > 智能感知与计算研究中心

	Learning a Robust Part-Aware Monocular 3D Human Pose Estimator via Neural Architecture Search
	Chen, Zerui 3,4; Huang, Yan 4; Yu, Hongyuan 3,4; Wang, Liang 1,2,3,4
刊名	INTERNATIONAL JOURNAL OF COMPUTER VISION
	2021-10-26
页码	20
关键词	Monocular 3D human pose estimation Heterogeneous human body parts Neural architecture search
ISSN号	0920-5691
DOI	10.1007/s11263-021-01525-0
通讯作者	Wang, Liang(wangliang@nlpr.ia.ac.cn)
英文摘要	Even though most existing monocular 3D human pose estimation methods achieve very competitive performance, they are limited in estimating heterogeneous human body parts with the same decoder architecture. In this work, we present an approach to build a part-aware 3D human pose estimator to better deal with these heterogeneous human body parts. Our proposed method consists of two learning stages: (1) searching suitable decoder architectures for specific parts and (2) training the part-aware 3D human pose estimator built with these optimized neural architectures. Consequently, our searched model is very efficient and compact and can automatically select a suitable decoder architecture to estimate each human body part. In comparison with previous state-of-the-art models built with ResNet-50 network, our method can achieve better performance and reduce 64.4% parameters and 8.5% FLOPs (multiply-adds). We validate the robustness and stability of our searched models by conducting extensive and rigorous ablation experiments. Our method can advance state-of-the-art accuracy on both the single-person and multi-person 3D human pose estimation benchmarks with affordable computational cost.
资助项目	National Key Research and Development Program of China[2018AAA0100400] ; National Natural Science Foundation of China[61633021] ; National Natural Science Foundation of China[61721004] ; National Natural Science Foundation of China[61806194] ; National Natural Science Foundation of China[U1803261] ; National Natural Science Foundation of China[61976132] ; Beijing Nova Program[Z201100006820079] ; Shandong Provincial Key Research andDevelopment Program[2019JZZY010119] ; Key Research Program of Frontier Sciences CAS[ZDBS-LY-JSC032] ; CAS-AIR
WOS研究方向	Computer Science
语种	英语
出版者	SPRINGER
WOS记录号	WOS:000711300700001
资助机构	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Beijing Nova Program ; Shandong Provincial Key Research andDevelopment Program ; Key Research Program of Frontier Sciences CAS ; CAS-AIR
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/46270]
专题	自动化研究所_智能感知与计算研究中心
通讯作者	Wang, Liang
作者单位	1.Chinese Acad Sci, Artificial Intelligence Res, Beijing, Peoples R China 2.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 4.CASIA, Ctr Res Intelligent Percept & Comp, NLPR, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Chen, Zerui,Huang, Yan,Yu, Hongyuan,et al. Learning a Robust Part-Aware Monocular 3D Human Pose Estimator via Neural Architecture Search[J]. INTERNATIONAL JOURNAL OF COMPUTER VISION,2021:20.
APA	Chen, Zerui,Huang, Yan,Yu, Hongyuan,&Wang, Liang.(2021).Learning a Robust Part-Aware Monocular 3D Human Pose Estimator via Neural Architecture Search.INTERNATIONAL JOURNAL OF COMPUTER VISION,20.
MLA	Chen, Zerui,et al."Learning a Robust Part-Aware Monocular 3D Human Pose Estimator via Neural Architecture Search".INTERNATIONAL JOURNAL OF COMPUTER VISION (2021):20.