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.

翻译：我们提出了一种新颖的单阶段端到端多人二维姿态估计算法，称为联合坐标回归与关联（JCRA），该算法无需任何后处理即可直接生成人体姿态关键点及其关联关系。所提算法具有快速、准确、高效且简洁的特点。单阶段端到端网络架构显著提升了JCRA的推理速度。同时，我们为编码器和解码器设计了对称网络结构，确保了关键点识别的高精度。该算法采用通过Transformer网络直接输出部位位置的架构，从而大幅提升了性能。在MS COCO和CrowdPose基准数据集上的大量实验表明，JCRA在准确率和效率上均优于现有最优方法。此外，JCRA达到了69.2的mAP值，且推理加速比现有最优自底向上算法快78%。该算法代码将公开发布。