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Multi-frame human pose estimation has long been a compelling and fundamentalproblem in computer vision. This task is challenging due to fast motion andpose occlusion that frequently occur in videos. State-of-the-art methods striveto incorporate additional visual evidences from neighboring frames (supportingframes) to facilitate the pose estimation of the current frame (key frame). Oneaspect that has been obviated so far, is the fact that current methods directlyaggregate unaligned contexts across frames. The spatial-misalignment betweenpose features of the current frame and neighboring frames might lead tounsatisfactory results. More importantly, existing approaches build upon thestraightforward pose estimation loss, which unfortunately cannot constrain thenetwork to fully leverage useful information from neighboring frames. To tacklethese problems, we present a novel hierarchical alignment framework, whichleverages coarse-to-fine deformations to progressively update a neighboringframe to align with the current frame at the feature level. We further proposeto explicitly supervise the knowledge extraction from neighboring frames,guaranteeing that useful complementary cues are extracted. To achieve thisgoal, we theoretically analyzed the mutual information between the frames andarrived at a loss that maximizes the task-relevant mutual information. Theseallow us to rank No.1 in the Multi-frame Person Pose Estimation Challenge onbenchmark dataset PoseTrack2017, and obtain state-of-the-art performance onbenchmarks Sub-JHMDB and Pose-Track2018. Our code is released athttps://github. com/Pose-Group/FAMI-Pose, hoping that it will be useful to thecommunity.

Temporal Feature Alignment and Mutual Information Maximization for Video-Based Human Pose Estimation