Diversified Virtual Camera Composition

  • Mike Preuss
  • Paolo Burelli
  • Georgios N. Yannakakis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7248)

Abstract

The expressive use of virtual cameras and the automatic generation of cinematics within 3D environments shows potential to extend the communicative power of films into games and virtual worlds. In this paper we present a novel solution to the problem of virtual camera composition based on niching and restart evolutionary algorithms that addresses the problem of diversity in shot generation by simultaneously identifying multiple valid camera camera configurations. We asses the performance of the proposed solution against a set of state-of-the-art algorithms in virtual camera optimisation.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Arijon, D.: Grammar of the Film Language. Silman-James Press, LA (1991)Google Scholar
  2. 2.
    Auger, A., Finck, S., Hansen, N., Ros, R.: BBOB 2010: Comparison Tables of All Algorithms on All Noiseless Functions. Technical Report RT-388, INRIA (September 2010)Google Scholar
  3. 3.
    Auger, A., Hansen, N.: A restart cma evolution strategy with increasing population size. In: Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2005, Edinburgh, UK, September 2-4, pp. 1769–1776. IEEE Press (2005)Google Scholar
  4. 4.
    Blinn, J.: Where Am I? What Am I Looking At? IEEE Computer Graphics and Applications 8(4), 76–81 (1988)CrossRefGoogle Scholar
  5. 5.
    Bourne, O., Sattar, A., Goodwin, S.: A Constraint-Based Autonomous 3D Camera System. Journal of Constraints 13(1-2), 180–205 (2008)MATHCrossRefGoogle Scholar
  6. 6.
    Burelli, P., Yannakakis, G.N.: Combining Local and Global Optimisation for Virtual Camera Control. In: IEEE Conference on Computational Intelligence and Games, p. 403 (2010)Google Scholar
  7. 7.
    Burelli, P., Yannakakis, G.N.: Global Search for Occlusion Minimisation in Virtual Camera Control. In: IEEE Congress on Evolutionary Computation, pp. 1–8. IEEE, Barcelona (2010)CrossRefGoogle Scholar
  8. 8.
    Christie, M., Olivier, P., Normand, J.M.: Camera Control in Computer Graphics. Computer Graphics Forum 27, 2197–2218 (2008)CrossRefGoogle Scholar
  9. 9.
    Drucker, S.M., Zeltzer, D.: Intelligent camera control in a virtual environment. In: Graphics Interface, pp. 190–199 (1994)Google Scholar
  10. 10.
    Hansen, N., Ostermeier, A.: Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation 9(2), 159–195 (2001)CrossRefGoogle Scholar
  11. 11.
    Hansen, N.: The cma evolution strategy: A tutorial, http://www.lri.fr/~hansen/cmatutorial.pdf (version of June 28, 2011)
  12. 12.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: IEEE Conference on Neural Networks, pp. 1942–1948 (1995)Google Scholar
  13. 13.
    Mersmann, O., Preuss, M., Trautmann, H., Bischl, B., Weihs, C.: Analyzing the bbob results by means of benchmarking concepts. Evolutionary Computation (accepted, 2012)Google Scholar
  14. 14.
    Mersmann, O., Bischl, B., Trautmann, H., Preuss, M., Weihs, C., Rudolph, G.: Exploratory landscape analysis. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, GECCO 2011, pp. 829–836. ACM, New York (2011)CrossRefGoogle Scholar
  15. 15.
    Olivier, P., Halper, N., Pickering, J., Luna, P.: Visual Composition as Optimisation. In: Artificial Intelligence and Simulation of Behaviour (1999)Google Scholar
  16. 16.
    Preuss, M.: Niching the cma-es via nearest-better clustering. In: Proceedings of the 12th Annual Conference Companion on Genetic and Evolutionary Computation, GECCO 2010, pp. 1711–1718. ACM (2010)Google Scholar
  17. 17.
    Preuss, M.: Improved Topological Niching for Real-Valued Global Optimization. In: Di Chio, C., et al. (eds.) EvoApplications 2012. LNCS, vol. 7248, pp. 386–395. Springer, Heidelberg (2012)Google Scholar
  18. 18.
    Preuss, M., Schönemann, L., Emmerich, M.: Counteracting genetic drift and disruptive recombination in (μ + /, λ)-EA on multimodal fitness landscapes. In: Proc. Genetic and Evolutionary Computation Conf. (GECCO 2005), vol. 1, pp. 865–872. ACM Press (2005)Google Scholar
  19. 19.
    Shir, O.M., Emmerich, M., Bäck, T.: Adaptive niche radii and niche shapes approaches for niching with the cma-es. Evolutionary Computation 18(1), 97–126 (2010)CrossRefGoogle Scholar
  20. 20.
    Stoean, C., Preuss, M., Stoean, R., Dumitrescu, D.: Multimodal optimization by means of a topological species conservation algorithm. IEEE Transactions on Evolutionary Computation 14(6), 842–864 (2010)CrossRefGoogle Scholar
  21. 21.
    Storn, R., Price, K.: Differential Evolution A Simple and Efficient Heuristic for global Optimization over Continuous Spaces. Journal of Global Optimization 11(4), 341–359 (1997)MathSciNetMATHCrossRefGoogle Scholar
  22. 22.
    Thawonmas, R., Oda, K., Shuda, T.: Rule-Based Camerawork Controller for Automatic Comic Generation from Game Log. In: Yang, H.S., Malaka, R., Hoshino, J., Han, J.H. (eds.) ICEC 2010. LNCS, vol. 6243, pp. 326–333. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  23. 23.
    Ware, C., Osborne, S.: Exploration and virtual camera control in virtual three dimensional environments. ACM SIGGRAPH 24(2), 175–183 (1990)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Mike Preuss
    • 1
  • Paolo Burelli
    • 2
  • Georgios N. Yannakakis
    • 2
  1. 1.Computational Intelligence Group, Dept. of Computer ScienceTechnische Universität DortmundDortmundGermany
  2. 2.Center for Computer Games ResearchIT University of CopenhagenCopenhagenDenmark

Personalised recommendations