Photo-Consistent Motion Blur Modeling for Realistic Image Synthesis

  • Huei-Yung Lin
  • Chia-Hong Chang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4319)


Motion blur is an important visual cue for the illusion of object motion. It has many applications in computer animation, virtual reality and augmented reality. In this work, we present a nonlinear imaging model for synthetic motion blur generation. It is shown that the intensity response of the image sensor is determined by the optical parameters of the camera and can be derived by a simple photometric calibration process. Based on the nonlinear behavior of the image intensity response, photo-realistic motion blur can be obtained and combined with real scenes with least visual inconsistency. Experiments have shown that the proposed method generates more photo-consistent results than the conventional motion blur model.


Augmented Reality Point Spread Function Intensity Response Computer Animation Motion Blur 
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.


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  1. 1.
    Lengyel, J.: The convergence of graphics and vision. Computer 31(7), 46–53 (1998)CrossRefGoogle Scholar
  2. 2.
    Kutulakos, K.N., Vallino, J.R.: Calibration-free augmented reality. IEEE Transactions on Visualization and Computer Graphics 4(1), 1–20 (1998)CrossRefGoogle Scholar
  3. 3.
    Potmesil, M., Chakravarty, I.: Modeling motion blur in computer-generated images. In: Proceedings of the 10th annual conference on Computer graphics and interactive techniques, pp. 389–399. ACM Press, New York (1983)CrossRefGoogle Scholar
  4. 4.
    Max, N.L., Lerner, D.M.: A two-and-a-half-d motion-blur algorithm. In: SIGGRAPH 1985: Proceedings of the 12th annual conference on Computer graphics and interactive techniques, pp. 85–93. ACM Press, New York (1985)CrossRefGoogle Scholar
  5. 5.
    Dachille, F., Kaufman, A.: High-degree temporal antialiasing. In: CA 2000: Proceedings of the Computer Animation, pp. 49–54 (2000)Google Scholar
  6. 6.
    Sung, K., Pearce, A., Wang, C.: Spatial-temporal antialiasing. IEEE Transactions on Visualization and Computer Graphics 08(2), 144–153 (2002)CrossRefGoogle Scholar
  7. 7.
    Brostow, G., Essa, I.: Image-based motion blur for stop motion animation. In: SIGGRAPH 2001 Conference Proceedings, ACM SIGGRAPH, pp. 561–566 (2001)Google Scholar
  8. 8.
    Wloka, M.M., Zeleznik, R.C.: Interactive real-time motion blur. The Visual Computer 12(6), 283–295 (1996)Google Scholar
  9. 9.
    Meinds, K., Stout, J., van Overveld, K.: Real-time temporal anti-aliasing for 3d graphics. In: Ertl, T. (ed.) VMV, pp. 337–344, Aka GmbH(2003)Google Scholar
  10. 10.
    Rush, A.: Nonlinear sensors impact digital imaging. Electronics Engineer (1998)Google Scholar
  11. 11.
    Forsyth, D., Ponce, J.: Computer Vision: A Modern Approach. Prentice-Hall, Englewood Cliffs (2003)Google Scholar
  12. 12.
    Debevec, P.E., Malik, J.: Recovering high dynamic range radiance maps from photographs. In: SIGGRAPH 1997: Proceedings of the 24th annual conference on Computer graphics and interactive techniques, pp. 369–378. ACM Press, New York (1997)CrossRefGoogle Scholar
  13. 13.
    Schanz, M., Nitta, C., Bussman, A., Hosticka, B.J., Wertheimer, R.K.: A high-dynamic-range cmos image sensor for automotive applications. IEEE Journal of Solid-State Circuits 35(7), 932–938 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Huei-Yung Lin
    • 1
  • Chia-Hong Chang
    • 1
  1. 1.Department of Electrical EngineeringNational Chung Cheng UniversityMin-Hsiung, Chia-YiTaiwan, R.O.C.

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