1 code implementation • ECCV 2020 • Ailing Zeng, Xiao Sun, Fuyang Huang, Minhao Liu, Qiang Xu, Stephen Lin
With the reduced dimensionality of less relevant body areas, the training set distribution within network branches more closely reflects the statistics of local poses instead of global body poses, without sacrificing information important for joint inference.
Ranked #20 on Monocular 3D Human Pose Estimation on Human3.6M
no code implementations • 9 Dec 2019 • Fuyang Huang, Ailing Zeng, Minhao Liu, Qiuxia Lai, Qiang Xu
In this paper, we propose a two-stage fully 3D network, namely \textbf{DeepFuse}, to estimate human pose in 3D space by fusing body-worn Inertial Measurement Unit (IMU) data and multi-view images deeply.
Ranked #5 on 3D Human Pose Estimation on Total Capture
no code implementations • 26 Dec 2018 • Fuyang Huang, Ailing Zeng, Minhao Liu, Jing Qin, Qiang Xu
Experimental results show that the proposed structure-aware 3D hourglass network is able to achieve a mean joint error of 7. 4 mm in MSRA and 8. 9 mm in NYU datasets, respectively.