Open AccessResearch on character Action Control Method Based on Multi Phase-Functioned Neural Network and State Machine
Author(s) -
Zhe Wang,
Xin Jian Lei,
Jing Li
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2031/1/012035
Subject(s) - character (mathematics) , action (physics) , frame (networking) , computer science , artificial neural network , control (management) , phase (matter) , natural (archaeology) , artificial intelligence , state (computer science) , value (mathematics) , machine learning , algorithm , mathematics , physics , telecommunications , geometry , archaeology , quantum mechanics , history
Aiming at the situation that the phase-functioned neural network can’t produce natural action or interact with the world poorly in some specific scenes, this paper proposes to divide different states according to the phase information of characters, and train several independent networks with different weights to predict the next frame state of characters. The experimental results show that, while the complexity of the model is reduced, the action generated by this method is more natural and fluent, which has good application value.