Abstract. 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, 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