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CLC number: TP391.9

On-line Access: 2013-07-05

Received: 2012-12-29

Revision Accepted: 2013-05-07

Crosschecked: 2013-06-06

Cited: 3

Clicked: 2846

Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE C 2013 Vol.14 No.7 P.542-550


Statistical learning based facial animation

Author(s):  Shibiao Xu, Guanghui Ma, Weiliang Meng, Xiaopeng Zhang

Affiliation(s):  National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China

Corresponding email(s):   aquathinker@gmail.com, xpzhang@nlpr.ia.ac.cn

Key Words:  Facial animation, Motion unit, Statistical learning, Realistic rendering, Pre-integration

Shibiao Xu, Guanghui Ma, Weiliang Meng, Xiaopeng Zhang. Statistical learning based facial animation[J]. Journal of Zhejiang University Science C, 2013, 14(7): 542-550.

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author="Shibiao Xu, Guanghui Ma, Weiliang Meng, Xiaopeng Zhang",
journal="Journal of Zhejiang University Science C",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Statistical learning based facial animation
%A Shibiao Xu
%A Guanghui Ma
%A Weiliang Meng
%A Xiaopeng Zhang
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%@ 1869-1951
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.CIDE1307

T1 - Statistical learning based facial animation
A1 - Shibiao Xu
A1 - Guanghui Ma
A1 - Weiliang Meng
A1 - Xiaopeng Zhang
J0 - Journal of Zhejiang University Science C
VL - 14
IS - 7
SP - 542
EP - 550
%@ 1869-1951
Y1 - 2013
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.CIDE1307

To synthesize real-time and realistic facial animation, we present an effective algorithm which combines image- and geometry-based methods for facial animation simulation. Considering the numerous motion units in the expression coding system, we present a novel simplified motion unit based on the basic facial expression, and construct the corresponding basic action for a head model. As image features are difficult to obtain using the performance driven method, we develop an automatic image feature recognition method based on statistical learning, and an expression image semi-automatic labeling method with rotation invariant face detection, which can improve the accuracy and efficiency of expression feature identification and training. After facial animation redirection, each basic action weight needs to be computed and mapped automatically. We apply the blend shape method to construct and train the corresponding expression database according to each basic action, and adopt the least squares method to compute the corresponding control parameters for facial animation. Moreover, there is a pre-integration of diffuse light distribution and specular light distribution based on the physical method, to improve the plausibility and efficiency of facial rendering. Our work provides a simplification of the facial motion unit, an optimization of the statistical training process and recognition process for facial animation, solves the expression parameters, and simulates the subsurface scattering effect in real time. Experimental results indicate that our method is effective and efficient, and suitable for computer animation and interactive applications.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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