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Estimating human pose from video is a task that receives considerableattention due to its applicability in numerous 3D fields. The complexity ofprior knowledge of human body movements poses a challenge to neural networkmodels in the task of regressing keypoints. In this paper, we address thisproblem by incorporating motion prior in an adversarial way. Different fromprevious methods, we propose to decompose holistic motion prior to joint motionprior, making it easier for neural networks to learn from prior knowledgethereby boosting the performance on the task. We also utilize a novelregularization loss to balance accuracy and smoothness introduced by motionprior. Our method achieves 9\% lower PA-MPJPE and 29\% lower acceleration errorthan previous methods tested on 3DPW. The estimator proves its robustness byachieving impressive performance on in-the-wild dataset.

Decomposed Human Motion Prior for Video Pose Estimation via Adversarial Training