  This paper studies the problem of multi-person pose estimation in a bottom-up
fashion. With a new and strong observation that the localization issue of the
center-offset formulation can be remedied in a local-window search scheme in an
ideal situation, we propose a multi-person pose estimation approach, dubbed as
LOGO-CAP, by learning the LOcal-GlObal Contextual Adaptation for human Pose.
Specifically, our approach learns the keypoint attraction maps (KAMs) from the
local keypoints expansion maps (KEMs) in small local windows in the first step,
which are subsequently treated as dynamic convolutional kernels on the
keypoints-focused global heatmaps for contextual adaptation, achieving accurate
multi-person pose estimation. Our method is end-to-end trainable with near
real-time inference speed in a single forward pass, obtaining state-of-the-art
performance on the COCO keypoint benchmark for bottom-up human pose estimation.
With the COCO trained model, our method also outperforms prior arts by a large
margin on the challenging OCHuman dataset.
