Human 3D posture detection and modeling based on automatic variational correction in multi-vision

This paper proposed a method for detecting 3D pose points inside the human body based on skin multi-person linear (SMPL) model and mapped 2D pose points inside the human body from multiple perspectives to 3D pose points in real scenes using a clustering algorithm in order to address issues such as continuous modeling jitter and local distortion of model results caused by the existing methods of constructing 3D human body models based on 2D human body surface pose points. The Kalman filter is introduced to denoise the attitude points of the human body. In the process of constructing a human 3D model based on 3D pose points, this paper corrects the gradient descent regression network based on an automatic variational method and constructs an end-to-end human 3D modeling network SMPL-VAE, which is more in line with the local modeling of human motion structure while maintaining the overall proportion. The test on the open data set Shelf revealed that the attitude points could be correctly matched for various targets, and the mean position error per joint (MPJPE) was improved by 3.88, 7.56, 12.88, respectively, compared with other methods. Additionally, the percentage of correct key points (PCK) was improved by 3.5, 6.91, and 9, respectively, compared with other methods.