A Survey on 3D Skeleton-Based Action Recognition Using Learning Method

Bin Ren1, Mengyuan Liu2, Runwei Ding1 and Hong Liu1

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

Sijie Yan, Yuanjun Xiong, Dahua Lin

Department of Information Engineering, The Chinese University of Hong Kong

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fys016, dhling@ie.cuhk.edu.hk, bitxiong@gmail.com

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Revisiting Skeleton-based Action Recognition

Haodong Duan1 Yue Zhao2 Kai Chen3;5 Dian Shao1 Dahua Lin1 Bo Dai4

1The Chinese University of HongKong 2The University of Texas at Austin

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3SenseTime Research 4S-Lab, Nanyang Technological University 5Shanghai AI Laboratory

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DeepPose: Human Pose Estimation via Deep Neural Networks

Alexander Toshev Christian Szegedy

Google

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1600 Amphitheatre Pkwy

Mountain View, CA 94043

toshev,szegedy@google.com

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Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields

Zhe Cao Tomas Simon Shih-En Wei Yaser Sheikh

The Robotics Institute, Carnegie Mellon University

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{zhecao,shihenw}@cmu.edu {tsimon,yaser}@cs.cmu.edu

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Convolutional Pose Machines

Shih-En Wei

shihenw@cmu.edu

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Varun Ramakrishna

vramakri@cs.cmu.edu

Takeo Kanade

Takeo.Kanade@cs.cmu.edu

Yaser Sheikh

yaser@cs.cmu.edu

The Robotics Institute

Carnegie Mellon University

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RMPE: Regional Multi-Person Pose Estimation

Hao-Shu Fang1 , Shuqin Xie1, Yu-Wing Tai2, Cewu Lu1§

1Shanghai Jiao Tong University, China 2 Tencent YouTu

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fhaoshu@gmail.com qweasdshu@sjtu.edu.cn yuwingtai@tencent.com lucewu@sjtu.edu.cn

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Stacked Hourglass Networks for Human Pose Estimation

Alejandro Newell, Kaiyu Yang, Jia Deng

University of Michigan, Ann Arbor