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

本文介绍了一种称为MultiPoseNet的新型底层多人姿态估计架构，它结合了多任务模型和新型分配方法，可同时处理人物检测、关键点检测、人物分割和姿态估计问题，并在COCO关键点数据集上超越了所有先前的底层方法，具有更高的精度和更快的速度，在至少4倍的速度下与最佳的自上而下方法相当。

MultiPoseNet：使用姿态残差网络的快速多人姿态估计