Abstract
Web-based 3D facial animation is an alternative of face to face communication. Animating 3D human faces is also a difficult task because of the substantive non-rigid facial motions and utmost human familiarity with facial expressions. This paper presents a novel piece-wise learning approach to 3D facial animation driven by facial motion capture data. The pipeline of our algorithm comprises three major parts: (1) data pre-processing, (2) facial region segmentation, and (3) facial deformation. We first present an effective pre-processing algorithm for non-rigid motion extraction and data alignment. Second, based on the statistical and kinematical analysis of motion capture data and the topological analysis of facial mesh, our system segments the facial regions by a two-layer clustering algorithm. The edges of segments are well considered using an adapted plane/space partition algorithm. During runtime, the stream of motion capture data and the 3D face model are efficiently fused by cluster-wise optimization. The experimental results show that our algorithm is not only realistic but also fast enough for real time applications.
This work is supported by National Natural Science Foundation (NSF) of China (No.60525108, No.60533090), 973 Program (2002CB312101), Science and Technology Project (STP) of Zhejiang Province (2005C13032, 2005C11001-05).
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References
Ou, K.-L., Chen, G.-D., Liu, C.-C., Liu, B.-J.: Instructional instruments for web group learning systems : The grouping, intervention, and strategy. In: Proc. of the Fifth annual SIGCSE/SIGCUE ITiCSE conference on Innovation and technology in computer science education, Helsinki, Finland, pp. 69–72 (2000)
Noh, J.-y., Neumann, U.: A Survey of facial modeling and Animation Techniques, USC Technical Report 99-705 (1998)
Parke, F.: Computer generated animation of faces. In: ACM National Conference, pp. 451–457 (1972)
Choe, B., Lee, H., Ko, H.: Performance-Driven Muscle-Based Facial Animation. The Journal of Visualization and Computer Animation 12(2), 67–79 (2001)
Sifakis, E., Neverov, I., Fedkiw, R.: Automatic Determination of Facial Muscle Activations from Sparse Motion Capture Marker Data. SIGGRAPH 2005, ACM TOG 24, 417–425 (2005)
Kalra, P., Mangili, A., Thalmann, N.M., Thalmann, D.: Simulation of Facial Muscle Actions Based on Rational Free From Deformations. Eurographics 11(3), 59–69 (1992)
Guenter, B., Grimm, C., Wood, D., Malvar H., Pighin, F.: Making Faces. In: SIGGRAPH 1998 Proceedings, pp. 55–66 (1998)
Zhang, L., Snavely, N., Curless, B., Seitz, S. M.: Spacetime faces: High-resolution capture for modeling and animation. In: ACM SIGGRAPH Proceedings, Los Angeles, CA, pp. 548–558 (2004)
Noh, J., Neumann, U.: Expression cloning. In: Proceeding of SIGGRAPH 2001, pp. 271–288 (2001)
Zhang, Q., Liu, Z., Guo, B., Terzopoulos, D., Shum, H.: Geometry-Driven Photorealistic Facial Expression Synthesis. IEEE Transactions on Visualization and Computer Graphics 12(1), 48–60 (2006)
Chuang, E., Bregler, C.: Moodswings: Expressive Speech Animation. ACM Transactions on Graphics 24(2) (April 2005)
Chai, J., Xiao, J., Hodgins, J.K.: Vision-based Control of 3D Facial Animation. In: Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation (July 2003)
Sánchez Lorenzo, M.A., Edge, J.D., King, S.A., Maddock, S.C.: Use and Re-use of Facial Motion Capture Data. In: Vision, Video and Graphics, VVG 2003, University of Bath, UK, pp. 135–142 (2003)
Joshi, P., Tien, W.C.: Learning Controls for Blend Shape Based Realistic Facial Animation. In: Proceedings of EUROGRAPHICS 2003, pp. 187–192 (2003)
Park, B., Chung, H., Nishita, T., Shin, S.Y.: A feature-based approach to facial expression cloning. Computer Animation and Virtual Worlds 6(3-4), 291–303 (2005)
Poggio, T., Giros, F.: A theory of networks for approximation and learning. Technical Report A.I. Memo No. 1140, Artificial Intelligence Lab, MIT, Cambridge, MA (July 1989)
Tenenbaum, J.B., de Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319–2323 (2000)
Roweis, S., Saul, L.: Nonlinear dimensionality reduction by locally linear embedding. Science 5500, 2323–2326 (2000)
Niyogi, P., Belkin, M.: Laplacian eigenmaps for dimensionality reduction and data representation, Technical Report, University of Chicago (January 2002)
Johnson, S.C.: Hierarchical Clustering Schemes. Psychometrika 2, 241–254 (1967)
Blum, H.: A transformation for extracting new descriptors of shape. In: Proceedings of the Symposium on Models for Perception of Speech and Visual Form, pp. 362–380. MIT Press, Cambridge, MA (1967)
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Wang, Y., Zhuang, Y., Xiao, J., Wu, F. (2008). A Piece-Wise Learning Approach to 3D Facial Animation. In: Leung, H., Li, F., Lau, R., Li, Q. (eds) Advances in Web Based Learning – ICWL 2007. ICWL 2007. Lecture Notes in Computer Science, vol 4823. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78139-4_37
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DOI: https://doi.org/10.1007/978-3-540-78139-4_37
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