  Multi-person pose estimation is fundamental to many computer vision tasks and
has made significant progress in recent years. However, few previous methods
explored the problem of pose estimation in crowded scenes while it remains
challenging and inevitable in many scenarios. Moreover, current benchmarks
cannot provide an appropriate evaluation for such cases. In this paper, we
propose a novel and efficient method to tackle the problem of pose estimation
in the crowd and a new dataset to better evaluate algorithms. Our model
consists of two key components: joint-candidate single person pose estimation
(SPPE) and global maximum joints association. With multi-peak prediction for
each joint and global association using graph model, our method is robust to
inevitable interference in crowded scenes and very efficient in inference. The
proposed method surpasses the state-of-the-art methods on CrowdPose dataset by
5.2 mAP and results on MSCOCO dataset demonstrate the generalization ability of
our method. Source code and dataset will be made publicly available.
