  Heatmap regression has become the most prevalent choice for nowadays human
pose estimation methods. The ground-truth heatmaps are usually constructed via
covering all skeletal keypoints by 2D gaussian kernels. The standard deviations
of these kernels are fixed. However, for bottom-up methods, which need to
handle a large variance of human scales and labeling ambiguities, the current
practice seems unreasonable. To better cope with these problems, we propose the
scale-adaptive heatmap regression (SAHR) method, which can adaptively adjust
the standard deviation for each keypoint. In this way, SAHR is more tolerant of
various human scales and labeling ambiguities. However, SAHR may aggravate the
imbalance between fore-background samples, which potentially hurts the
improvement of SAHR. Thus, we further introduce the weight-adaptive heatmap
regression (WAHR) to help balance the fore-background samples. Extensive
experiments show that SAHR together with WAHR largely improves the accuracy of
bottom-up human pose estimation. As a result, we finally outperform the
state-of-the-art model by +1.5AP and achieve 72.0AP on COCO test-dev2017, which
is com-arable with the performances of most top-down methods. Source codes are
available at https://github.com/greatlog/SWAHR-HumanPose.
