Skip to main content

Visual Behavior Definition for 3D Crowd Animation through Neuro-evolution

  • Conference paper
Hybrid Artificial Intelligence Systems (HAIS 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8480))

Included in the following conference series:

  • 1986 Accesses

Abstract

This paper addresses the problem of creating crowd based scenes in animated films automatically. The main problem in this area is how to provide a natural way for the animator or director to define what they want the crowd to do. To this end, we propose here a hybrid neuro-evolutionary scheme where the artists regulate the behavior that is desired from the crowd by drawing colored lines and areas within a scenario. These elements are then transformed into energy based aggregative fitness functions that can be used to evaluate the behaviors of the individuals within the crowd during the evolutionary process that produces the controller for all the characters and, consequently, determines the behavior of the crowd as a whole. The approach has been tested on several different real scenes within the workflow of a local animation film company and the results it produced were very satisfactory.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ali, S., Nishino, K., Manocha, D., Shah, M.: Modeling, Simulation and Visual Analysis of Crowds: A Multidisciplinary Perspective, vol. 11. Springer, Heidelberg (2013)

    Book  Google Scholar 

  2. Banerjee, B., Kraemer, L.: Evaluation and Comparison of Multi-agent Based Crowd Simulation Systems. In: Dignum, F. (ed.) Agents for Games and Simulations II. LNCS, vol. 6525, pp. 53–66. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  3. Caamaño, P., Bellas, F., Becerra, J.A., Duro, R.J.: Evolutionary algorithm characterization in real parameter optimization problems. Applied Soft Computing 13(4), 1902–1921 (2013)

    Article  Google Scholar 

  4. Corchado, E., Wóniak, M., Abraham, A., de Carvalho, A.C.P.L.F., Snášel, V.: Recent trends in intelligent data analysis. Neurocomputing 126, 1–2 (2011); Recent trends in Intelligent Data Analysis Selected papers of the The 6th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2011) Online Data Processing Including a selection of papers from the International Conference on Adaptive and Intelligent Systems 2011 (ICAIS 2011)

    Google Scholar 

  5. Floreano, D., Dürr, P., Mattiussi, C.: Neuroevolution: from architectures to learning. Evolutionary Intelligence 1(1), 47–62 (2008)

    Article  Google Scholar 

  6. Yu, H., Liu, H., Yang, X.: Evolutionary modeling approach for crowd animation. In: 2012 International Conference on Computer Science and Information Processing (CSIP), pp. 237–241. IEEE (2012)

    Google Scholar 

  7. Li, T.-Y., Wang, C.-C.: An evolutionary approach to crowd simulation. In: Autonomous Robots and Agents, pp. 119–126. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  8. Parent, R.: Computer animation: algorithms and techniques. Newnes (2012)

    Google Scholar 

  9. Patil, S., Berg, J.V.D., Curtis, S., Lin, M.C., Manocha, D.: Directing crowd simulations using navigation fields. IEEE Transactions on Visualization and Computer Graphics 17(2), 244–254 (2011)

    Article  Google Scholar 

  10. Pelechano, N., Allbeck, J.M., Badler, N.I.: Controlling individual agents in high-density crowd simulation. In: Proceedings of the 2007 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pp. 99–108. Eurographics Association (2007)

    Google Scholar 

  11. 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)

    Article  MATH  MathSciNet  Google Scholar 

  12. Sung, M., Gleicher, M., Chenney, S.: Scalable behaviors for crowd simulation. In: Computer Graphics Forum, vol. 23, pp. 519–528. Wiley Online Library (2004)

    Google Scholar 

  13. Szarowicz, A., Amiguet-Vercher, J., Forte, P., Briggs, J., Gelepithis, P., Remagnino, P.: The Application of AI to Automatically Generated Animation. In: Stumptner, M., Corbett, D.R., Brooks, M. (eds.) Canadian AI 2001. LNCS (LNAI), vol. 2256, pp. 487–494. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  14. Thalmann, D.: Crowd Simulation. John Wiley & Sons, Inc. (2007)

    Google Scholar 

  15. Vigueras, G., Orduña, J.M., Lozano, M., Jégou, Y.: A scalable multiagent system architecture for interactive applications. Science of Computer Programming 78(6), 715–724 (2013)

    Article  Google Scholar 

  16. Woźniak, M., Graña, M., Corchado, E.: A survey of multiple classifier systems as hybrid systems. Information Fusion 16, 3–17 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Fernandez, B., Monroy, J., Bellas, F., Duro, R.J. (2014). Visual Behavior Definition for 3D Crowd Animation through Neuro-evolution. In: Polycarpou, M., de Carvalho, A.C.P.L.F., Pan, JS., Woźniak, M., Quintian, H., Corchado, E. (eds) Hybrid Artificial Intelligence Systems. HAIS 2014. Lecture Notes in Computer Science(), vol 8480. Springer, Cham. https://doi.org/10.1007/978-3-319-07617-1_32

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-07617-1_32

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07616-4

  • Online ISBN: 978-3-319-07617-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics