Full Text:   <2596>

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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: 7599

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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journal="Journal of Zhejiang University Science C",
publisher="Zhejiang University Press & Springer",

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%A Guanghui Ma
%A Weiliang Meng
%A Xiaopeng Zhang
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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
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SP - 542
EP - 550
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PB - Zhejiang University Press & Springer
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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


[1]Blanz, V., Basso, C., Poggio, T., Vetter, T., 2003. Reanimating faces in images and video. Comput. Graph. For., 22(3):641-650.

[2]Blinn, J.F., 1977. Models of light reflection for computer synthesized pictures. ACM SIGGRAPH Comput. Graph., 11(2):192-198.

[3]Buck, I., Finkelstein, A., Jacobs, C., Klein, A., Salesin, D.H., Seims, J., Szeliski, R., Toyama, K., 2006. Performance-Driven Hand-Drawn Animation. Proc. 1st Int. Symp. on Non-photorealistic Animation and Rendering, p.101-108.

[4]Chai, X.J., Shan, S.G., Gao, W., Chen, X.L., 2005. Example-based learning for automatic face alignment. J. Softw., 16(5):718-726 (in Chinese).

[5]Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J., 1995. Active shape models—their training and application. Comput. Vis. Image Understand., 61(1):38-59.

[6]Cootes, T.F., Edwards, G.J., Taylor, C.J., 2001. Active Appearance Models. IEEE Trans. Pattern Anal. Mach. Intell., 23(6):681-685.

[7]Deng, Z.G., Chiang, P.Y., Fox, P., Neumann, U., 2006. Animating Blend Shape Faces by Cross-Mapping Motion Capture Data. Proc. Symp. on Interactive 3D Graphics and Games, p.43-48.

[8]d′Eon, E., Luebke, D., Enderton, E., 2007. Efficient Rendering of Human Skin. Proc. 18th Eurographics Conf. on Rendering Techniques, p.147-157.

[9]Ekman, P., Friesen, W.V., Hager, J.C., 2002. Facial Action Coding System: the Manual. A Human Face. Research Nexus, Salt Lake City, p.1-197.

[10]Guenter, B., Grimm, C., Wood, D., Malvar, H., Pighin, F., 2006. Making Faces. ACM SIGGRAPH, p.55-66.

[11]Jimenez, J., Sundstedt, V., Gutierrez, D., 2009. Screen-space perceptual rendering of human skin. ACM Trans. Appl. Percept., 6(4), Article 23.

[12]Lance, W., 1990. Performance-Driven Facial Animation. ACM SIGGRAPH, p.235-242.

[13]Lee, Y.C., Terzopoulos, D., Waters, K., 1995. Realistic Modeling for Facial Animation. Proc. 22nd Annual Conf. on Computer Graphics and Interactive Techniques, p.55-62.

[14]Little, A.C., Hancock, P.J.B., DeBruine, L.M., Jones, B.C., 2012. Adaptation to antifaces and the perception of correct famous identity in an average face. Front. Psychol., 3:19.

[15]Matthews, I., Baker, S., 2004. Active appearance models revisited. Int. J. Comput. Vis., 60(2):135-164.

[16]Parke, F.I., 1972. Computer Generated Animation of Faces. Proc. ACM Annual Conf., p.451-457.

[17]Penner, E., Borshukov, G., 2011. Pre-integrated Skin Shading. In: Engel, W. (Ed.), GPU Pro 2: Advanced Rendering Techniques. A K Peters/CRC Press, p.41-54.

[18]Pighin, F., Hecker, J., Lischinski, D., Szeliski, R., Salesin, D.H., 2005. Synthesizing Realistic Facial Expressions from Photographs. ACM SIGGRAPH, p.75-84.

[19]Viola, P., Jones, M., 2001. Rapid Object Detection Using a Boosted Cascade of Simple Features. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, p.511-518.

[20]Weise, T., Bouaziz, S., Li, H., Pauly, M., 2011. Realtime performance-based facial animation. ACM Trans. Graph., 30(4), Article 77, p.1-10.

[21]Yao, J.F., Chen, Q., 2008. Survey on computer facial expression animation technology. Appl. Res. Comput., 25(11):3233-3237.

[22]Zhang, Q.S., Chen, G.L., 2003. Realistic 3D human facial animation. J. Softw., 14(3):643-650 (in Chinese).

[23]Zhang, Q.S., Liu, Z.C., Guo, B.N., Demetri, T., Shum, H.Y., 2006. Geometry-driven photorealistic facial expression synthesis. IEEE Trans. Visual. Comput. Graph., 12(1):48-60.

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