  Pose tracking is an important problem that requires identifying unique human
pose-instances and matching them temporally across different frames of a video.
However, existing pose tracking methods are unable to accurately model temporal
relationships and require significant computation, often computing the tracks
offline. We present an efficient Multi-person Pose Tracking method, KeyTrack,
that only relies on keypoint information without using any RGB or optical flow
information to track human keypoints in real-time. Keypoints are tracked using
our Pose Entailment method, in which, first, a pair of pose estimates is
sampled from different frames in a video and tokenized. Then, a
Transformer-based network makes a binary classification as to whether one pose
temporally follows another. Furthermore, we improve our top-down pose
estimation method with a novel, parameter-free, keypoint refinement technique
that improves the keypoint estimates used during the Pose Entailment step. We
achieve state-of-the-art results on the PoseTrack'17 and the PoseTrack'18
benchmarks while using only a fraction of the computation required by most
other methods for computing the tracking information.
