3D human pose estimation in motion is a hot research direction in the field of computer vision. However, the performance of the algorithm is affected by the complexity of 3D spatial information, self-occlusion of human body, mapping uncertainty and other problems. In this paper, we propose a 3D human joint localization method based on multi-stage regression depth network and 2D to 3D point mapping algorithm. First of all, we use a single RGB image as the input, through the introduction of heatmap and multi-stage regression to constantly optimize the coordinates of human joint points. Then we input the 2D joint points into the mapping network for calculation, and get the coordinates of 3D human body joint points, and then to complete the 3D human body pose estimation task. The MPJPE of the algorithm in Human3.6 M dataset is 40.7. The evaluation of dataset shows that our method has obvious advantages.

运动中的 3D 人体姿态估计是计算机视觉领域的一个热门研究方向。但是，算法的性能受到3D空间信息的复杂性、人体自遮挡、映射不确定性等问题的影响。在本文中，我们提出了一种基于多级回归深度网络和 2D 到 3D 点映射算法的 3D 人体关节定位方法。首先，我们使用单个RGB图像作为输入，通过引入热图和多阶段回归来不断优化人体关节点的坐标。然后我们将2D关节点输入映射网络进行计算，得到3D人体关节点的坐标，从而完成3D人体姿态估计任务。该算法在Human3.6 M数据集中的MPJPE为40.7。