Abstract: To accurately capture the posture of animation characters in virtual vision and optimize the user's experience when wearing virtual vision equipment, the hybrid Gaussian model has gained wide attention. However, various types of animation show an exponential growth trend, and the hybrid Gaussian model is prone to low-dimensional explosion when processing these single frames. Based on the mixed Gaussian model, this study conducts animation character gesture recognition experiments on the Disert data set to solve these problems. Meanwhile, it is improved by frame rate reduction method to generate fusion algorithm. In this paper, the video is first grayened and filtered, and the model feature points of the image are marked. Then the weight learning rate is introduced and added to the set of pixels, and then the peak signal-to-noise ratio of Wronsky function is adjusted by changing the parameters. Then similar image sets are extracted and the structure elements are opened and closed. Finally, the proposed algorithm is applied to Disert data set. Meanwhile, the prediction accuracy of PSO is tested and compared with fusion algorithm. A total of 400 experiments were conducted, and the prediction accuracy of the fusion algorithm reached 392 times, with an accuracy of 98.0%. The accuracy of PSO is close to that of fusion algorithm (88.2%). It is verified that the suggested model can identify the four common gestures of cartoon characters well, and users will get a good viewing experience.

Jing Yang and Hao Zhang, “Application of VR Technology Based on Gesture Recognition in Animation-form Capture” International Journal of Advanced Computer Science and Applications(IJACSA), 14(7), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140785

@article{Yang2023,
title = {Application of VR Technology Based on Gesture Recognition in Animation-form Capture},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140785},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140785},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {7},
author = {Jing Yang and Hao Zhang}
}