  We observe that human poses exhibit strong group-wise structural correlation
and spatial coupling between keypoints due to the biological constraints of
different body parts. This group-wise structural correlation can be explored to
improve the accuracy and robustness of human pose estimation. In this work, we
develop a self-constrained prediction-verification network to characterize and
learn the structural correlation between keypoints during training. During the
inference stage, the feedback information from the verification network allows
us to perform further optimization of pose prediction, which significantly
improves the performance of human pose estimation. Specifically, we partition
the keypoints into groups according to the biological structure of human body.
Within each group, the keypoints are further partitioned into two subsets,
high-confidence base keypoints and low-confidence terminal keypoints. We
develop a self-constrained prediction-verification network to perform forward
and backward predictions between these keypoint subsets. One fundamental
challenge in pose estimation, as well as in generic prediction tasks, is that
there is no mechanism for us to verify if the obtained pose estimation or
prediction results are accurate or not, since the ground truth is not
available. Once successfully learned, the verification network serves as an
accuracy verification module for the forward pose prediction. During the
inference stage, it can be used to guide the local optimization of the pose
estimation results of low-confidence keypoints with the self-constrained loss
on high-confidence keypoints as the objective function. Our extensive
experimental results on benchmark MS COCO and CrowdPose datasets demonstrate
that the proposed method can significantly improve the pose estimation results.
