2019-10-15-tang19a.md

title	crossref	abstract	layout	series	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	publisher	container-title	volume	genre	issued	pdf	extras
An Articulated Structure-aware Network for 3D Human Pose Estimation	acml19	In this paper, we propose a new end-to-end articulated structure-aware network to regress 3D joint coordinates from the given 2D joint detections. The proposed method is capable of dealing with hard joints well that usually fail existing methods. Specifically, our framework cascades a refinement network with a basic network for two types of joints, and employs a attention module to simulate a camera projection model. In addition, we propose to use a random enhancement module to intensify the constraints between joints. Experimental results on the Human3.6M and HumanEva databases demonstrate the effectiveness and flexibility of the proposed network, and errors of hard joints and bone lengths are significantly reduced, compared with state-of-the-art approaches.	inproceedings	Proceedings of Machine Learning Research	tang19a	0	An Articulated Structure-aware Network for 3D Human Pose Estimation	48	63	48-63	48	false	Tang, Zhenhua and Zhang, Xiaoyan and Hou, Junhui	given family Zhenhua Tang given family Xiaoyan Zhang given family Junhui Hou	2019-10-15		PMLR	Proceedings of The Eleventh Asian Conference on Machine Learning	101	inproceedings	date-parts 2019 10 15	http://proceedings.mlr.press/v101/tang19a/tang19a.pdf	label link Supplementary PDF http://proceedings.mlr.press/v101/tang19a/tang19a-supp.pdf