Online Motion Style Transfer for Interactive Character Control

Autor:	Tang, Yingtian, Liu, Jiangtao, Zhou, Cheng, Li, Tingguang
Rok vydání:	2022
Předmět:	Computer Science - Graphics Computer Science - Artificial Intelligence Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Motion style transfer is highly desired for motion generation systems for gaming. Compared to its offline counterpart, the research on online motion style transfer under interactive control is limited. In this work, we propose an end-to-end neural network that can generate motions with different styles and transfer motion styles in real-time under user control. Our approach eliminates the use of handcrafted phase features, and could be easily trained and directly deployed in game systems. In the experiment part, we evaluate our approach from three aspects that are essential for industrial game design: accuracy, flexibility, and variety, and our model performs a satisfying result. Comment: 8 pages, 5 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2203.16393 Zobrazit plný text záznamu View this record from Arxiv