  Although many approaches for multi-human pose estimation in videos have shown
profound results, they require densely annotated data which entails excessive
man labor. Furthermore, there exists occlusion and motion blur that inevitably
lead to poor estimation performance. To address these problems, we propose a
method that leverages an attention mask for occluded joints and encodes
temporal dependency between frames using transformers. First, our framework
composes different combinations of sparsely annotated frames that denote the
track of the overall joint movement. We propose an occlusion attention mask
from these combinations that enable encoding occlusion-aware heatmaps as a
semi-supervised task. Second, the proposed temporal encoder employs transformer
architecture to effectively aggregate the temporal relationship and
keypoint-wise attention from each time step and accurately refines the target
frame's final pose estimation. We achieve state-of-the-art pose estimation
results for PoseTrack2017 and PoseTrack2018 datasets and demonstrate the
robustness of our approach to occlusion and motion blur in sparsely annotated
video data.
