Skip to main content
Springer Nature Link
Log in

Guiding flows for controlling crowds

  • Original Article
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

In this paper, we present a novel method for controlling massive crowds by using control particles. Our method differs from previous ones that generate attraction (or repelling) forces around the control particles. Instead of doing this, we create a steady-state, flow-like control field that guides the crowd to move along with the control particles. Our control field can be naturally incorporated into the original simulation by using density-based weighted blending. Although we focus on simulation methods that use dynamic potential functions, our method can also be used to improve the controllability of agent-based simulation methods. Since the control particles can be easily manipulated by traditional key-framing, our method provides animators with an intuitive interface for manipulating the position of crowd over time. We illustrate the effectiveness of our method on several examples.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Chenney, S.: Flow tiles. In: 2004 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pp. 233–242 (2004)

  2. Courty, N., Corpetti, T.: Crowd motion capture. Comput. Animat. Virtual Worlds 18, 361–370 (2007)

    Article  Google Scholar 

  3. Funge, J., Tu, X., Terzopoulos, D.: Cognitive modeling: Knowledge, reasoning and planning for intelligent characters. ACM Trans. Graph. 18, 29–38 (1999)

    Google Scholar 

  4. Goldenstein, S., Karavelas, M., Metaxas, D., Guibas, L., Aaron, E., Goswami, A.: Scalable nonlinear dynamical systems for agent steering and crowd simulation. Comput. Graph. 11(1), 111 (2001)

    Google Scholar 

  5. Hong, J., Kim, C.: Controlling fluid animation with geometric potential. Comput. Animat. Virtual Worlds 15, 147–157 (2004)

    Article  Google Scholar 

  6. Hughes, R.L.: The flow of human crowds. Annu. Rev. Fluid Mech. 35, 169–182 (2003)

    Article  Google Scholar 

  7. Jin, X., Xu, J., Wang, C., Huang, S., Zhang, J.: Interactive control of large-crowd navigation in virtual environments using vector fields. IEEE Comput. Graph. Appl. 28(6), 37–46 (2008)

    Article  Google Scholar 

  8. Kwon, T., Lee, K.H., Lee, J., Takahashi, S.: Group motion editing. ACM Trans. Graph. 27(3), 111 (2008)

    Article  Google Scholar 

  9. Lamarche, F., Donikian, S.: Crowd of virtual humans: A new approach for real time navigation in complex and structured environments. Comput. Graph. Forum 23(3), 509–518 (2004)

    Article  Google Scholar 

  10. Lee, K.H., Choi, M.G., Hong, Q., Lee, J.: Group behavior from video: A data-driven approach to crowd simulation. In: Eurographics/ACM SIGGRAPH Symposium on Computer Animation, p. 111 (2007)

  11. Lerner, A., Chrysanthou, Y., Lischinski, D.: Crowds by example. Comput. Graph. Forum 26(3), 655–664 (2007)

    Article  Google Scholar 

  12. Lerner, A., Fitusi, E., Chrysanthou, Y., Cohen-Or, D.: Fitting behaviors to pedestrian simulations. In: Proceedings of the 2009 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pp. 199–208 (2009)

  13. Lin, M.C., Sud, A., van den Berg, J., Gayle, R., Curtis, S., Yeh, H.n, Guy, S., Andersen, E., Patil, S., Sewall, J., Manocha, D.: Real-time path planning and navigation for multi-agent and crowd simulations. In: Motion in Games: First International Workshop, MIG 2008, Utrecht, The Netherlands, June 14–17, 2008. Revised Papers, pp. 23–32 (2008)

  14. Massive Software Inc.: Massive Software (2006). http://massivesoftware.com

  15. McNamara, A., Treuille, A., Popovic̀, Z., Stam, J.: Fluid control using the adjoint method. ACM Trans. Graph. 23(3), 449–456 (2004)

    Article  Google Scholar 

  16. Musse, S.R., Thalmann, D.: A model of human crowd behavior: Group inter-relationship and collision detection analysis. In: Computer Animation and Simulation, pp. 39–51 (1997)

  17. Musse, S.R., Jung, C.R., Jacques, J. Jr., Braun, A.: Using computer vision to simulate the motion of virtual agents. Comput. Animat. Virtual Worlds 18, 83–93 (2007)

    Article  Google Scholar 

  18. Park, S.I., Shin, H.J., Kim, T.H., Shin, S.Y.: On-line motion blending for real-time locomotion generation. Comput. Animat. Virtual Worlds 15(3–4), 125–138 (2004)

    Article  Google Scholar 

  19. Pettre, J., Laumond, J.-P., Thalmann, D.: A navigation graph for real-time crowd animation on multilayered and uneven terrain. In: First International Workshop on Crowd Simulation (2005)

  20. Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C++. Cambridge University Press, Cambridge (2001)

    Google Scholar 

  21. Reynolds, C.: Flocks, herds, and schools: A distributed behavioral model. Comput. Graph. 21(4), 25–34 (1987). (SIGGRAPH’87 Conference Proceedings)

    Article  MathSciNet  Google Scholar 

  22. Reynolds, C.: Steering behaviors for autonomous characters. In: Proc. of Game Developers Conference (GDC) 1999, pp. 763–782 (1999)

  23. Shao, W., Terzopoulos, D.: Autonomous pedestrians. Graph. Models 69, 246–274 (2007). (ACM SIGGRAPH/Eurographics Symposium on Computer Animation)

    Article  Google Scholar 

  24. Shi, L., Yu, Y.: Controllable smoke animation with guiding objects. ACM Trans. Graph. 24, 140–164 (2005)

    Article  MathSciNet  Google Scholar 

  25. Sung, M., Gleicher, M., Chenny, S.: Scalable behaviors for crowd simulation. Comput. Graph. Forum 23(3), 519–528 (2004)

    Article  Google Scholar 

  26. Sung, M., Kovar, L., Gleicher, M.: Fast and accurate goal-directed motion synthesis for crowds. In: Proceedings of the 2005 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pp. 291–300 (2005)

  27. Treuille, A., Cooper, S., Popovic, Z.: Continuum crowds. ACM Trans. Graph. 11(1), 111 (2006)

    Google Scholar 

  28. Wejchert, J., Haumann, D.: Animation aerodynamics. In: International Conf. on Computer Graphics and Interactive Techniques, pp. 19–22 (2004)

  29. Wojtan, C., Mucha, P.J., Turk, G.: Keyframe control of complex particle systems using the adjoint method. In: Eurographics/ACM SIGGRAPH Symposium on Computer Animation, pp. 1–9 (2006)

Download references

Author information

Authors and Affiliations

  1. Macrograph Co., Seoul, Korea

    Min Je Park

Authors
  1. Min Je Park
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Min Je Park.

Electronic Supplementary Material

Guiding Flows for Controlling Crowds (MPG 18.8 MB)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Park, M.J. Guiding flows for controlling crowds. Vis Comput 26, 1383–1391 (2010). https://doi.org/10.1007/s00371-009-0415-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-009-0415-4