Research on simulation of 3D human animation vision technology based on an enhanced machine learning algorithm

This paper provides an in-depth analysis and study of the simulation of 3D human animation visualization techniques by enhancing machine learning algorithms. Based on the statistical analysis of the data obtained from different measurement methods, the extraction of human body feature parameters based on millimeter-wave point cloud data is realized, and the 3D reconstruction and simulation of the human body are realized using parametric human modeling software. In video-based action recognition, most methods are data-driven and use deep networks to automatically learn features of the entire video image. In this process, specific research on human actions is not included or reflected. However, human action recognition is a processing of the semantic level of video content. Realizing universal human action recognition requires a semantic understanding of human behavior. Firstly, the geometric feature analysis of the 3D scanned human model is performed to extract the human body shape characteristic parameters, and the research on the analysis and estimation methods of body shape characteristic parameters is carried out to establish the human body shape parameter relationship model; then, the millimeter-wave point cloud is calculated and measured, the Li group features extracted using the group skeletal representation model with high data dimensionality, to be able to process the high-dimensional data, while reducing the complexity of the recognition process and speeding up the computation, feature learning and classification are performed with convolutional neural networks. To verify the better library portability and robustness of the method in this paper, the method was tested on a self-built human action database in the laboratory, and an average recognition rate of 97.26% was achieved. Meanwhile, this paper investigates the natural interaction application of virtual characters in a virtual learning environment based on human action recognition. Four testers tested the virtual human–computer interaction system of this paper, respectively, and the final test results show that the system has flexibility and stability.