  In this paper, we present MultiPoseNet, a novel bottom-up multi-person pose
estimation architecture that combines a multi-task model with a novel
assignment method. MultiPoseNet can jointly handle person detection, keypoint
detection, person segmentation and pose estimation problems. The novel
assignment method is implemented by the Pose Residual Network (PRN) which
receives keypoint and person detections, and produces accurate poses by
assigning keypoints to person instances. On the COCO keypoints dataset, our
pose estimation method outperforms all previous bottom-up methods both in
accuracy (+4-point mAP over previous best result) and speed; it also performs
on par with the best top-down methods while being at least 4x faster. Our
method is the fastest real time system with 23 frames/sec. Source code is
available at: https://github.com/mkocabas/pose-residual-network
