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.

我们引入了一种新颖的一阶段端到端的多人2D姿态估计算法，称为联合坐标回归和关联（JCRA），它能够在不需要任何后处理的情况下生成人体姿势关节和关联。提出的算法快速、准确、有效、简单，具有利用端到端网络架构显著提高推理速度的特点。同时，我们为编码器和解码器设计了对称的网络结构，确保可以高精度地识别关键点。它遵循通过转换网络直接输出部分位置的架构，从而在性能方面有了显着改进。在MS COCO和CrowdPose基准测试上进行了广泛的实验，结果表明JCRA在精度和效率方面均优于现有技术。此外，JCRA在推理加速方面比先前最先进的自底向上算法快78%，并且该算法的代码将公开发布。

多人姿态估计的联合坐标回归和关联纯神经网络方法