Self-supervised Keypoint Correspondences for Multi-Person Pose Estimation and Tracking in Videos
Umer Rafi, Andreas Doering, Bastian Leibe, Juergen Gall
Video annotation is expensive and time consuming. Consequently, datasets for multi-person pose estimation and tracking are less diverse and have more sparse annotations compared to large scale image datasets for human pose estimation. This makes it challenging to learn deep learning based models for associating keypoints across frames that are robust to nuisance factors such as motion blur and occlusions for the task of multi-person pose tracking. %It is clear that leveraging large scale human pose annotations from single images benefits frame level human pose estimation in videos. This raises the question can large scale human pose annotations from single images benefit muti-person pose tracking. To address this issue, we propose an approach that relies on keypoint correspondences for associating persons in videos. Instead of training the network for estimating keypoint correspondences on video data, it is trained on a large scale image datasets for human pose estimation using self-supervision. Combined with a top-down framework for human pose estimation, we use keypoints correspondences to (i) recover missed pose detections (ii) associate pose detections across video frames. Our approach achieves state-of-the-art results for temporal pose estimation and multi-person pose tracking on the PosTrack $2017$ and PoseTrack $2018$ data sets. "