  Multi-person pose estimation and tracking serve as crucial steps for video
understanding. Most state-of-the-art approaches rely on first estimating poses
in each frame and only then implementing data association and refinement.
Despite the promising results achieved, such a strategy is inevitably prone to
missed detections especially in heavily-cluttered scenes, since this
tracking-by-detection paradigm is, by nature, largely dependent on visual
evidences that are absent in the case of occlusion. In this paper, we propose a
novel online approach to learning the pose dynamics, which are independent of
pose detections in current fame, and hence may serve as a robust estimation
even in challenging scenarios including occlusion. Specifically, we derive this
prediction of dynamics through a graph neural network~(GNN) that explicitly
accounts for both spatial-temporal and visual information. It takes as input
the historical pose tracklets and directly predicts the corresponding poses in
the following frame for each tracklet. The predicted poses will then be
aggregated with the detected poses, if any, at the same frame so as to produce
the final pose, potentially recovering the occluded joints missed by the
estimator. Experiments on PoseTrack 2017 and PoseTrack 2018 datasets
demonstrate that the proposed method achieves results superior to the state of
the art on both human pose estimation and tracking tasks.
