  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. 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 multi-frame
pose estimation and multi-person pose tracking on the PosTrack $2017$ and
PoseTrack $2018$ data sets.
