An Integral Pose Regression System for the ECCV2018 PoseTrack Challenge
This work addresses 3D human pose estimation for computer vision applications, but it is incremental as it builds on existing methods for a specific challenge.
The authors tackled 3D human pose estimation for the ECCV2018 PoseTrack Challenge using an integral pose regression method, achieving 47mm MPJPE on the CHALL_H80K test dataset and placing second in the competition.
For the ECCV 2018 PoseTrack Challenge, we present a 3D human pose estimation system based mainly on the integral human pose regression method. We show a comprehensive ablation study to examine the key performance factors of the proposed system. Our system obtains 47mm MPJPE on the CHALL_H80K test dataset, placing second in the ECCV2018 3D human pose estimation challenge. Code will be released to facilitate future work.