A Point-2s reinforcement learning biomimetic model for estimating and analyzing human 3D motion posture

Abstract

With the rapid progress of computer vision and artificial intelligence, the accuracy of estimating and analyzing human body movements and postures has always been a highly focused research field. However, current methods still have some shortcomings in accurately estimating the pose of 3D human movements. This study aims to propose an effective method to accurately estimate the motion posture of 3D human activities using new technologies of deep learning and neural networks. Firstly, based on previous research, this paper analyzes the problems and challenges of insufficient accuracy in current 3D human motion pose estimation methods, limited capture of 3D spatial information in deep video data, and inability to capture subtle motion details. Then, in response to the problem of disorder in point clouds, an innovative SMPL human Point-2 s reinforcement learning framework was constructed using the VIBE network to estimate the pose of RGB in the NTU dataset. 24 Point-2 s biomimetic algorithm joints were sampled from a distance using the FPS of PointNet++in SMPL, and an index was established based on relative positions to ensure that other joint points are in the same position. Finally, these joint points were input into the 2 s-AGCN network to construct a complete Point-2 s model. The research results indicate that the proposed Point-2 s model has achieved good results in accurately estimating the motion posture of 3D human activities, and effectively solves the problem of disorder in point clouds converted from deep videos. Compared to traditional methods, this research model has significantly improved accuracy and stability. The practical application of this method will provide useful references and guidance for research and method development in related fields.