Face Modeling and Wrinkle Simulation Using Convolution Surface

  • Qing He
  • Minglei Tong
  • Yuncai Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4069)


This paper presents a new method to simulate wrinkles on individual face model, applying convolution surface to face modeling. A generic face mesh is deformed and the texture image is computed using image-based modeling technique. The deformed mesh is then convolved with a kernel function to generate a convolution surface, and wrinkles are generated by modulating the surface with a designed profile function. The pre-computed texture is mapped onto the convolution surface to enhance the realism. Experimental results show that our method can generate wrinkles with different patterns by regulating some parameters of the profile function.


Profile Function Computer Animation Skin Aging Texture Model Facial Deformation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ansari, A.N., Abdel-Mottaleb, M.: 3D Face Modeling Using Two Orthogonal Views and A Generic Face Model. In: Proceedings of 2003 International Conference on Multimedia and Expo, pp. 289–292 (2003)Google Scholar
  2. 2.
    Sherstyuk, A.: Convolution Surfaces in Computer Graphics. PhD dissertation, Monash University, School Of Computer Science and Software Engineering (1999)Google Scholar
  3. 3.
    Ahlberg, J.: CANDIDE-3 – an updated parameterized face, Report No. LiTH-ISY-R-2326, Department of Electrical Engineering, Linköping University, Sweden (2001)Google Scholar
  4. 4.
    Bloomenthal, J., Lim, C.: Skeletal methods of shape manipulation. In: Proceedings of International Conference on Shape Modeling and Applications 1999, vol. 267, pp. 44–47 (1999)Google Scholar
  5. 5.
    Bloomenthal, J., Shoemake, K.: Convolution Surfaces. SIGGRAPH Proceedings 25(4), 251–256 (1991)CrossRefGoogle Scholar
  6. 6.
    Boissieux, L., Kiss, G., Thalmann, N.M., Kalra, P.: Simulation of Skin Aging and Wrinkles with Cosmetics Insight. In: Proc. Eurographics Workshop on Computer Animation and Simulation 2000, pp. 15–27 (2000)Google Scholar
  7. 7.
    Tong, M., Liu, Y., Huang, T.S.: Recover Human Pose from Monocular Image Under Weak Perspective Projection. In: Sebe, N., Lew, M., Huang, T.S. (eds.) HCI/ICCV 2005. LNCS, vol. 3766, pp. 36–46. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  8. 8.
    Volino, P., Thalmann, N.M.: Fast Geometrical Wrinkles on Animated Surfaces. In: Proc. WSCG 1999 (1999)Google Scholar
  9. 9.
    Oeltze, S., Preim, B.: Visualization of Vasculature with Convolution Surfaces: Method, Validation and Evaluation. IEEE Transactions on Medical Imaging 24(4), 540–548 (2005)CrossRefGoogle Scholar
  10. 10.
    Jin, X., Tai, C.-L.: Convolution surfaces for Line Skeletons with Polynomial Weight Distributions. Journal of Grapgics Tools ACM Press 6(3), 17–28 (2001)MATHGoogle Scholar
  11. 11.
    Bando, Y., Kuratate, T., Nishita, T.: A Simple Method for Modeling Wrinkles on Human Skin. In: Proc. Pacific Graphics 2002, pp. 166–175 (2002)Google Scholar
  12. 12.
    Wu, Y., Kalra, P., Thalmann, N.M.: Physically-based Wrinkle Simulation & Skin Rendering. In: Proc. Eurographics Workshop on Computer Animation and Simulation 1997, pp. 69–79 (1997)Google Scholar
  13. 13.
    Wu, Y., Thalmann, N.M., Thalmann, D.: A dynamic wrinkle model in facial animation and skin aging. J. Visualization and Computer Animation 6, 195–202 (1998)CrossRefGoogle Scholar
  14. 14.
    Zhang, Y., Sim, T.: Realistic and efficient wrinkle simulation using an anatomy-based face model with adaptive refinement. In: Proceedings of Computer Graphics International 2005, pp. 3–10 (2005)Google Scholar
  15. 15.
    Zhuang, Y., Su, C., Huang, L., Wu, F.: Subdivision Feedback Based 3D Facial Modeling for E-learning. In: Zhou, W., Nicholson, P., Corbitt, B., Fong, J. (eds.) ICWL 2003. LNCS, vol. 2783, pp. 218–229. Springer, Heidelberg (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Qing He
    • 1
  • Minglei Tong
    • 1
  • Yuncai Liu
    • 1
  1. 1.Institute of Image Processing and Pattern RecognitionShanghai Jiaotong UniversityShanghaiChina

Personalised recommendations