Joint Coordinate Regression and Association For Multi-Person Pose
  Estimation, A Pure Neural Network Approach
Dongyang Yu Yunshi Xie Wangpeng An Li Zhang Yufeng Yao
We introduce a novel one-stage end-to-end multi-person 2D pose estimation
algorithm, known as Joint Coordinate Regression and Association (JCRA), that
produces human pose joints and associations without requiring any
post-processing. The proposed algorithm is fast, accurate, effective, and
simple. The one-stage end-to-end network architecture significantly improves
the inference speed of JCRA. Meanwhile, we devised a symmetric network
structure for both the encoder and decoder, which ensures high accuracy in
identifying keypoints. It follows an architecture that directly outputs part
positions via a transformer network, resulting in a significant improvement in
performance. Extensive experiments on the MS COCO and CrowdPose benchmarks
demonstrate that JCRA outperforms state-of-the-art approaches in both accuracy
and efficiency. Moreover, JCRA demonstrates 69.2 mAP and is 78\% faster at
inference acceleration than previous state-of-the-art bottom-up algorithms. The
code for this algorithm will be publicly available.