Zihao Zhang1,2, Lei Hu1,2,*, Xiaoming Deng3,*, Shihong Xia1,2
1Institute of Computing Technology, 2University of Chinese Academy of Sciences, 3Institute of Software CAS, *Same contribution
Abstract
3D human pose estimation is a fundamental problem in artificial intelligence, and it has wide applications in AR/VR, HCI and robotics. However, human pose estimation from point clouds still suffers from noisy points and estimated jittery artifacts because of handcrafted-based point cloud sampling and single-frame-based estimation strategies. In this paper, we present a new perspective on the 3D human pose estimation method from point cloud sequences. To sample effective point clouds from input, we design a differentiable point cloud sampling method built on density-guided attention mechanism. To avoid the jitter caused by previous 3D human pose estimation problems, we adopt temporal information to obtain more stable results. Experiments on the ITOP dataset and the NTURGBD dataset demonstrate that all of our contributed components are effective, and our method can achieve state-of-the-art performance.
Code
https://github.com/Hmslab/Adapose
Video
Paper
Zhang Z, Hu L, Deng X, et al. Sequential 3D Human Pose Estimation Using Adaptive Point Cloud Sampling Strategy[C]//IJCAI. 2021: 1330-1337.
Cite
@inproceedings{zhang2021sequential,
title={Sequential 3D Human Pose Estimation Using Adaptive Point Cloud Sampling Strategy.},
author={Zhang, Zihao and Hu, Lei and Deng, Xiaoming and Xia, Shihong},
booktitle={IJCAI},
pages={1330--1337},
year={2021}
}