Abstract: In terms of the problems such as mutual occlusion, self-occlusion, sports equipment occlusion and complex background interference among athletes in motion scenes, a high-resolution feature generation recovery network is proposed in this paper. The attention fusion mechanism is introduced to screen the useful feature information channels. The deconvolution and multi-scale feature fusion modules are added to deal with the pose estimation tasks for small target portraits and large and medium-sized target portraits in a hierarchical manner. The adversarial module is designed and generated to complete and predict the missing parts to obtain the keypoint heat map, the keypoint connection mode is determined through the pose skeleton and the optimal matching algorithm, and the visual pose estimation results are output. Experimental results on MSCOCO and Crowd Pose datasets have showed that the pose estimation method is more effective in complex motion scenes.