  Single-stage multi-person human pose estimation (MPPE) methods have shown
great performance improvements, but existing methods fail to disentangle
features by individual instances under crowded scenes. In this paper, we
propose a bounding box-level instance representation learning called BoIR,
which simultaneously solves instance detection, instance disentanglement, and
instance-keypoint association problems. Our new instance embedding loss
provides a learning signal on the entire area of the image with bounding box
annotations, achieving globally consistent and disentangled instance
representation. Our method exploits multi-task learning of bottom-up keypoint
estimation, bounding box regression, and contrastive instance embedding
learning, without additional computational cost during inference. BoIR is
effective for crowded scenes, outperforming state-of-the-art on COCO val (0.8
AP), COCO test-dev (0.5 AP), CrowdPose (4.9 AP), and OCHuman (3.5 AP). Code
will be available at https://github.com/uyoung-jeong/BoIR
