Optimization of VR human-computer game interaction based on improved PIFPAF algorithm and binocular vision

To make virtual reality human-computer games more accurate and provide users with an immersive gaming experience, the study combines the method of improved part intensity field and part association field (PIFPAF) with binocular vision to optimize the interaction of VR human-computer games. The experimental results indicated that the PIFPAF algorithms performed relatively well with number of errors and target keypoint correlation of 0.22 and 0.97, respectively. In terms of processing speed, the algorithm performed faster in both 640×480 and 320×240 resolutions, with 13 fps and 19 fps, respectively. Among the five predefined gestures, the ʻʻpointingʼʼ gesture was recognized correctly the largest number of times in 30 test sessions, with 29 successful identifications. In contrast, the ʻʻclenched fistʼʼ gesture had the fewest correct recognitions, totaling 26. The success of the suggested approach is confirmed by the experimental findings, which show that the optimized human-computer interaction system has high accuracy and processing speed. This study offers a fresh approach to the advancement of human-computer interaction technology and encourages technological integration innovation in the realm of virtual reality human-computer gaming.