no code implementations • CVPR 2019 • Ching-Hang Chen, Ambrish Tyagi, Amit Agrawal, Dylan Drover, Rohith MV, Stefan Stojanov, James M. Rehg
Additionally, to learn from 2D poses "in the wild", we train an unsupervised 2D domain adapter network to allow for an expansion of 2D data.
Ranked #72 on 3D Human Pose Estimation on MPI-INF-3DHP (AUC metric)
no code implementations • 22 Aug 2018 • Dylan Drover, Rohith MV, Ching-Hang Chen, Amit Agrawal, Ambrish Tyagi, Cong Phuoc Huynh
We present a weakly supervised approach to estimate 3D pose points, given only 2D pose landmarks.
no code implementations • 19 Dec 2017 • Huan-Cheng Hsu, Ching-Hang Chen, Hsiao-Rong Tyan, Hong-Yuan Mark Liao
With the hierarchical cross feature maps, an HCN can effectively uncover additional semantic features which could not be discovered by a conventional CNN.
no code implementations • CVPR 2017 • Ching-Hang Chen, Deva Ramanan
While many approaches try to directly predict 3D pose from image measurements, we explore a simple architecture that reasons through intermediate 2D pose predictions.
Ranked #293 on 3D Human Pose Estimation on Human3.6M