A study of real-time operations by converting human skeleton coordinates to digital avatars

This study aims to develop a real-time motion recognition system that translates skeletal human movements into a virtual environment. This will be achieved through the use of advanced tech-niques for the accurate capture of human skeletons and coordinate conversion. This paper investi-gates the acquisition and processing of motion data for virtual characters using depth cameras to obtain depth information. This study identifies six specific actions: left kick, right kick, left punch, right punch, squatting, and sitting. The experimental process successfully integrated RGB+D cameras, Media Pipe, and OpenCV into Unreal Engine models to capture and display human skeletal and joint positions in real-time. The experimental results show that the system achieved a precision of 100% for all motion detections, with an accuracy of more than 94%. How-ever, the recall rate for specific actions was lower, reaching 88%.