  Deep learning methods have achieved excellent performance in pose estimation,
but the lack of robustness causes the keypoints to change drastically between
similar images. In view of this problem, a stable heatmap regression method is
proposed to alleviate network vulnerability to small perturbations. We utilize
the correlation between different rows and columns in a heatmap to alleviate
the multi-peaks problem, and design a highly differentiated heatmap regression
to make a keypoint discriminative from surrounding points. A maximum stability
training loss is used to simplify the optimization difficulty when minimizing
the prediction gap of two similar images. The proposed method achieves a
significant advance in robustness over state-of-the-art approaches on two
benchmark datasets and maintains high performance.
