Open AccessVirtual Human Motion Extension Based on Bayesian Network
Author(s) -
Qi Shi,
Tinxin Xu,
Liang Ma,
Jianxun Liu
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/790/1/012086
Subject(s) - motion (physics) , computer science , artificial intelligence , computer vision , construct (python library) , virtual actor , extension (predicate logic) , structure from motion , motion field , motion estimation , bayesian network , virtual reality , programming language
Aiming at the problem of virtual human motion reuse, the concept of motion extension is proposed, which includes lengthways extension and transverse extension. The lengthways extension extends motion along the time, and generates more frames from existing motion. The transverse extension extends motion to other characters, and multiple characters motion is generated by single character motion. Each motion in the group is similar and different from each other. Motion extension provides unified solution to important issues such as motion prediction, motion repair, and group motion. We construct the Bayesian network of the virtual human motion, which studies and reduces the dimension of the virtual human motion, and propose a method of motion extension based on Bayesian network. Experiments show that our method can generate more realistic virtual human motion based on existing motion, and provide support for the application of virtual human in the fields of bio-engineering, medicine and military.