  This paper presents a novel end-to-end framework with Explicit box Detection
for multi-person Pose estimation, called ED-Pose, where it unifies the
contextual learning between human-level (global) and keypoint-level (local)
information. Different from previous one-stage methods, ED-Pose re-considers
this task as two explicit box detection processes with a unified representation
and regression supervision. First, we introduce a human detection decoder from
encoded tokens to extract global features. It can provide a good initialization
for the latter keypoint detection, making the training process converge fast.
Second, to bring in contextual information near keypoints, we regard pose
estimation as a keypoint box detection problem to learn both box positions and
contents for each keypoint. A human-to-keypoint detection decoder adopts an
interactive learning strategy between human and keypoint features to further
enhance global and local feature aggregation. In general, ED-Pose is
conceptually simple without post-processing and dense heatmap supervision. It
demonstrates its effectiveness and efficiency compared with both two-stage and
one-stage methods. Notably, explicit box detection boosts the pose estimation
performance by 4.5 AP on COCO and 9.9 AP on CrowdPose. For the first time, as a
fully end-to-end framework with a L1 regression loss, ED-Pose surpasses
heatmap-based Top-down methods under the same backbone by 1.2 AP on COCO and
achieves the state-of-the-art with 76.6 AP on CrowdPose without bells and
whistles. Code is available at https://github.com/IDEA-Research/ED-Pose.
