@inproceedings{33dd2890fcb04b469659905297b3817e,
title = "Data-driven facial animation via hypergraph learning",
abstract = "Data-driven facial animation has attracted much attention in recent years. Existing facial animation methods may not preserve the topology structure, and cannot achieve a natural face. This paper proposes a new data-driven facial animation method based on hypergraph learning. It drives a neutral face to a certain expression face. This paper assumes that neutral face has similar topology with the expression face, we compute the alignment laplacian matrix using hypergraph learning. To get a natural face, we add a constraint item which is consisted of a set of motion data. Experiment results demonstrate that our method can achieve a natural expression face. And the results show the superiority over the state-of-art.",
keywords = "Facial animation, Hypergraph learning, Manifold learning",
author = "Xi Li and Jun Yu and Fei Gao and Jian Zhang",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 ; Conference date: 09-10-2016 Through 12-10-2016",
year = "2017",
month = feb,
day = "6",
doi = "10.1109/SMC.2016.7844280",
language = "英语",
series = "2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "442--445",
booktitle = "2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings",
address = "美国",
}