In general, human pose estimation methods are categorized into two approaches
according to their architectures: regression (i.e., heatmap-free) and
heatmap-based methods. The former one directly estimates precise coordinates of
each keypoint using convolutional and fully-connected layers. Although this
approach is able to detect overlapped and dense keypoints, unexpected results
can be obtained by non-existent keypoints in a scene. On the other hand, the
latter one is able to filter the non-existent ones out by utilizing predicted
heatmaps for each keypoint. Nevertheless, it suffers from quantization error
when obtaining the keypoint coordinates from its heatmaps. In addition, unlike
the regression one, it is difficult to distinguish densely placed keypoints in
an image. To this end, we propose a hybrid model for single-stage multi-person
pose estimation, named HybridPose, which mutually overcomes each drawback of
both approaches by maximizing their strengths. Furthermore, we introduce
self-correlation loss to inject spatial dependencies between keypoint
coordinates and their visibility. Therefore, HybridPose is capable of not only
detecting densely placed keypoints, but also filtering the non-existent
keypoints in an image. Experimental results demonstrate that proposed
HybridPose exhibits the keypoints visibility without performance degradation in
terms of the pose estimation accuracy.

本研究提出了一个称为 HybridPose 的混合模型，通过最大化两种方法的优点来互相克服它们的缺点，同时引入自相关损失来注入关键点坐标和可见性之间的空间依赖性，从而在不降低姿势估计准确性的情况下展示了关键点的可见性。