  Previous video-based human pose estimation methods have shown promising
results by leveraging aggregated features of consecutive frames. However, most
approaches compromise accuracy to mitigate jitter or do not sufficiently
comprehend the temporal aspects of human motion. Furthermore, occlusion
increases uncertainty between consecutive frames, which results in unsmooth
results. To address these issues, we design an architecture that exploits the
keypoint kinematic features with the following components. First, we
effectively capture the temporal features by leveraging individual keypoint's
velocity and acceleration. Second, the proposed hierarchical transformer
encoder aggregates spatio-temporal dependencies and refines the 2D or 3D input
pose estimated from existing estimators. Finally, we provide an online
cross-supervision between the refined input pose generated from the encoder and
the final pose from our decoder to enable joint optimization. We demonstrate
comprehensive results and validate the effectiveness of our model in various
tasks: 2D pose estimation, 3D pose estimation, body mesh recovery, and sparsely
annotated multi-human pose estimation. Our code is available at
https://github.com/KyungMinJin/HANet.
