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

翻译：单阶段多人人体姿态估计（MPPE）方法已展现出显著的性能提升，但现有方法在拥挤场景下无法按个体实例解耦特征。本文提出一种基于边界框级别的实例表示学习方法BoIR，该方法同时解决实例检测、实例解耦和实例-关键点关联问题。我们提出的新实例嵌入损失函数能在整个图像区域提供基于边界框标注的学习信号，从而实现全局一致且解耦的实例表示。该方法利用多任务学习联合进行自底向上的关键点估计、边界框回归以及对比实例嵌入学习，推理时无需额外计算成本。BoIR在拥挤场景下表现优异，在COCO验证集（AP提升0.8）、COCO测试集（AP提升0.5）、CrowdPose（AP提升4.9）和OCHuman（AP提升3.5）上均超越现有最优方法。代码将发布于https://github.com/uyoung-jeong/BoIR