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From regular images to animated heads: A least squares approach

Part of the Lecture Notes in Computer Science book series (LNCS,volume 1406)


We show that we can effectively fit arbitrarily complex animation models to noisy image data. Our approach is based on leastsquares adjustment using of a set of progressively finer control triangulations and takes advantage of three complementary sources of information: stereo data, silhouette edges and 2-D feature points.

In this way, complete head models—including ears and hair—can be acquired with a cheap and entirely passive sensor, such as an ordinary video camera. They can then be fed to existing animation software to produce synthetic sequences.


  • Feature Point
  • Stereo Pair
  • Facial Animation
  • Surface Triangulation
  • Control Mesh

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  1. A. E. Beaton and J.W. Turkey. The Fitting of Power Series, Meaning Polynomials, Illustrated on Band-Spectroscopic Data. Technometrics, 16:147–185, 1974.

    MATH  CrossRef  Google Scholar 

  2. A. Blake and M. Isard. 3D Position Attitude and Shape Input Using Video Tracking of Hands and Lips. In Computer Graphics, SIGGRAPH Proceedings, pages 71–8, July 1994.

    Google Scholar 

  3. T.F. Cootes and C.J. Taylor. Locating Objects of Varying Shape Using Statistical Feature Detectors. In European Conference on Computer Vision, Cambridge, England, April 1996.

    Google Scholar 

  4. D. DeCarlo and D. Metaxas. The Integration of Optical Flow and Deformable Models with Applications to Human Face Shape and Motion Estimation. In Conference on Computer Vision and Pattern Recognition, pages 231–38, 1996.

    Google Scholar 

  5. F. Devernay and O. D. Faugeras. Computing Differential Properties of 3-D Shapes from Stereoscopic Images without 3-D Models. In Conference on Computer Vision and Pattern Recognition, pages 208–13, Seattle, WA, June 1994.

    Google Scholar 

  6. O.D. Faugeras, Q.-T. Luong, and S.J. Maybank. Camera self-calibration: theory and experiments. In European Conference on Computer Vision, pages 321–334, Santa-Margerita, Italy, 1992.

    Google Scholar 

  7. P. Fua. A Parallel Stereo Algorithm that Produces Dense Depth Maps and Preserves Image Features. Machine Vision and Applications, 6(1):35–49, Winter 1993.

    CrossRef  Google Scholar 

  8. P. Fua. From Multiple Stereo Views to Multiple 3-D Surfaces. International Journal of Computer Vision, 24(1):19–35, 1997.

    CrossRef  Google Scholar 

  9. P. Fua and C. Brechbühler. Imposing Hard Constraints on Deformable Models Through Optimization in Orthogonal Subspaces. Computer Vision and Image Understanding, 24(1):19–35, February 1997.

    Google Scholar 

  10. P.E. Gill, W. Murray, and M.H. Wright. Practical Optimization. Academic Press, London a.o., 1981.

    Google Scholar 

  11. T.S. Jebara and A. Pentland. Parametrized Structure from Motion for 3D Adaptive Feedback Tracking of Faces. In Conference on Computer Vision and Pattern Recognition, pages 144–150, Porto Rico, June 1997.

    Google Scholar 

  12. P. Kalra, A. Mangili, N. Magnenat Thalmann, and D. Thalmann. Simulation of Facial Muscle Actions Based on Rational Free Form Deformations. In Eurographics, 1992.

    Google Scholar 

  13. M. Kass, A. Witkin, and D. Terzopoulos. Snakes: Active Contour Models. International Journal of Computer Vision, 1(4):321–331, 1988.

    CrossRef  Google Scholar 

  14. Y. G. Leclerc and A. F. Bobick. The Direct Computation of Height from Shading. In Conference on Computer Vision and Pattern Recognition, Lahaina, Maui, Hawaii, June 1991.

    Google Scholar 

  15. Y. Lee, D. Terzopoulos, and K. Waters. Realistic Modeling for Facial Animation. In Computer Graphics, SIGGRAPH Proceedings, pages 191–198, Los Angeles, CA, August 1995.

    Google Scholar 

  16. B. Murtagh and M.A. Saunders. Minos 5.4 User's Guide. Technical Report SOL 83-20R, Department of Operations Research, Stanford University, 1983.

    Google Scholar 

  17. M. Pollefeys, R. Koch, and L. VanGool. Self-Calibration and Metric Reconstruction In Spite of Varying and Unknown Internal Camera Parameters. In International Conference on Computer Vision, 1998.

    Google Scholar 

  18. W.H. Press, B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling. Numerical Recipes, the Art of Scientific Computing. Cambridge U. Press, Cambridge, MA, 1986.

    Google Scholar 

  19. M. Proesmans, L. Van Gool, and A. Oosterlinck. Active acquisition of 3D shape for Moving Objects. In International Conference on Image Processing, Lausanne, Switzerland, September 1996.

    Google Scholar 

  20. L. Tang and T.S. Huang. Analysis-based facial expression synthesis. ICIP-III, 94:98–102.

    Google Scholar 

  21. B. Triggs. Autocalibration and the Absolute Quadric. In Conference on Computer Vision and Pattern Recognition, pages 609–614, 1997.

    Google Scholar 

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© 1998 Springer-Verlag Berlin Heidelberg

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Fua, P., Miccio, C. (1998). From regular images to animated heads: A least squares approach. In: Burkhardt, H., Neumann, B. (eds) Computer Vision — ECCV'98. ECCV 1998. Lecture Notes in Computer Science, vol 1406. Springer, Berlin, Heidelberg.

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