Advertisement

A Crowd Modeling Framework for Socially Plausible Animation Behaviors

  • Seung In Park
  • Chao Peng
  • Francis Quek
  • Yong Cao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7660)

Abstract

This paper presents a framework for crowd modeling that produces socially plausible animation behaviors. Our high-level behavioral model is able to produce appropriate animated behavior that includes synchronized body-orientation and gesture of individual actors within the simulation. Because the model operationalizes a well-founded social-linguistic Common Ground (CG) theory of human interaction, the behavior chains form meaningful interactions among the actors. The model includes micro-behaviors relating to CG theory, and macro-behavior relating to the animation context. This allows reuse of the micro-behaviors as animation contexts change and flexible adaptation to different animation contexts.

Keywords

Crowd simulation behavior control character animation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    van den Berg, J., Lin, M., Manocha, D.: Reciprocal velocity obstacles for real-time multi-agent navigation. In: IEEE Intl. Conf. on Robotics and Automation, ICRA 2008, pp. 1928–1935 (May 2008)Google Scholar
  2. 2.
    van den Berg, J., Guy, S.J., Snape, J., Lin, M.C., Manocha, D.: RVO2 library: Reciprocal collision avoidance for real-time multi-agent simulation (2011), http://gamma.cs.unc.edu/RVO2/
  3. 3.
    Brogan, D.C., Johnson, N.L.: Realistic human walking paths. In: Proc. of the 16th Intl. Conf. on Computer Animation and Social Agents, CASA 2003, pp. 94–101. IEEE Computer Society (2003)Google Scholar
  4. 4.
    Cassell, J., Vilhjálmsson, H.H., Bickmore, T.: Beat: the behavior expression animation toolkit. In: Proc. of the 28th Annual Conf. on Computer Graphics and Interactive Techniques, SIGGRAPH 2001, pp. 477–486. ACM (2001)Google Scholar
  5. 5.
    Clark, H.H., Brennan, S.: Grounding in communication, pp. 127–149 (1991)Google Scholar
  6. 6.
    Coleman, J.S., James, J.: The equilibrium size distribution of freely-forming groups. Sociometry 24(1), 36–45 (1961)CrossRefGoogle Scholar
  7. 7.
    Endrass, B., André, E., Rehm, M., Lipi, A.A., Nakano, Y.: Culture-related differences in aspects of behavior for virtual characters across Germany and Japan. In: Proc. of the 10th Intl. Conf. on Autonomous Agents and Multiagent Systems, AAMAS 2011, vol. 2, pp. 441–448 (2011)Google Scholar
  8. 8.
    Fiorini, P., Shillert, Z.: Motion planning in dynamic environments using velocity obstacles. International Journal of Robotics Research 17, 760–772 (1998)CrossRefGoogle Scholar
  9. 9.
    Helbing, D., Moln, P., Farkas, I.J., Bolay, K.: Self-organizing pedestrian movement. Environment and Planning B: Planning and Design 28(3), 361–383 (2001)CrossRefGoogle Scholar
  10. 10.
    Hoogendoorn, M., Soumokil, J.: Evaluation of virtual agents utilizing theory of mind in a real time action game. In: Proc. of the 9th Intl. Conf. on Autonomous Agents and Multiagent Systems, AAMAS 2010, vol. 1, pp. 59–66 (2010)Google Scholar
  11. 11.
    Hughes, R.L.: A continuum theory for the flow of pedestrians. Transportation Research Part B: Methodological 36(6), 507–535 (2002)CrossRefGoogle Scholar
  12. 12.
    James, J.: The Distribution of Free-Forming Small Group Size. American Sociological Review 18(5), 569–570 (1953)CrossRefGoogle Scholar
  13. 13.
    Jan, D., Herrera, D., Martinovski, B., Novick, D., Traum, D.: A Computational Model of Culture-Specific Conversational Behavior. In: Pelachaud, C., Martin, J.-C., André, E., Chollet, G., Karpouzis, K., Pelé, D. (eds.) IVA 2007. LNCS (LNAI), vol. 4722, pp. 45–56. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  14. 14.
    Kapadia, M., Singh, S., Hewlett, W., Faloutsos, P.: Egocentric affordance fields in pedestrian steering. In: Proc. of the 2009 Symposium on Interactive 3D Graphics and Games, I3D 2009, pp. 215–223. ACM (2009)Google Scholar
  15. 15.
    Lau, M., Kuffner, J.J.: Behavior planning for character animation. In: Proc. of the 2005 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, SCA 2005, pp. 271–280. ACM (2005)Google Scholar
  16. 16.
    Lemercier, S., Jelic, A., Kulpa, R., Hua, J., Fehrenbach, J., Degond, P., Appert-Rolland, C., Donikian, S., Pettré, J.: Realistic following behaviors for crowd simulation. Comp. Graph. Forum 31(2pt2), 489–498 (2012)CrossRefGoogle Scholar
  17. 17.
    Li, Y., Wang, T., Shum, H.Y.: Motion texture: a two-level statistical model for character motion synthesis. ACM Trans. Graph. 21(3), 465–472 (2002)CrossRefGoogle Scholar
  18. 18.
    Liu, C.K., Hertzmann, A., Popović, Z.: Composition of complex optimal multi-character motions. In: Proc. of the 2006 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, SCA 2006, pp. 215–222 (2006)Google Scholar
  19. 19.
    Moussaïd, M., Perozo, N., Garnier, S., Helbing, D., Theraulaz, G.: The Walking Behaviour of Pedestrian Social Groups and Its Impact on Crowd Dynamics. PLoS ONE 5(4), e10047+ (2010)Google Scholar
  20. 20.
    Musse, S.R., Thalmann, D.: A Model of Human Crowd Behavior: Group Inter-Relationship and Collision Detection Analysis. In: Workshop Computer Animation and Simulation of Eurographics, pp. 39–52 (1997)Google Scholar
  21. 21.
    Narain, R., Golas, A., Curtis, S., Lin, M.C.: Aggregate dynamics for dense crowd simulation. ACM Trans. Graph. 28, 122:1–122:8 (2009)Google Scholar
  22. 22.
    Park, S.I., Quek, F., Cao, Y.: Modeling agent social joint actions via micro and macro coordination strategies. To Appear in: Proc. of 2012 IEEE/WIC/ACM Intl. Conf. on Web Intelligence and Intelligent Agent Technology, WI-IAT 2012 (2012)Google Scholar
  23. 23.
    Park, S.I., Quek, F., Cao, Y.: Modeling social groups in crowds using common ground theory. To appear in: Proc. of the 2012 Winter Simulation Conference, WSC 2012 (December 2012)Google Scholar
  24. 24.
    Pelachaud, C.: Multimodal expressive embodied conversational agents. In: Proc. of the 13th Annual ACM Intl. Conf. on Multimedia, MULTIMEDIA 2005, pp. 683–689 (2005)Google Scholar
  25. 25.
    Pelechano, N.: Crowd simulation incorporating agent psychological models, roles and communication. In: Proc. of the First Intl. Workshop on Crowd Simulation, pp. 21–30 (2005)Google Scholar
  26. 26.
    Peng, C., Park, S.I., Cao, Y., Tian, J.: A Real-Time System for Crowd Rendering: Parallel LOD and Texture-Preserving Approach on GPU. In: Allbeck, J.M., Faloutsos, P. (eds.) MIG 2011. LNCS, vol. 7060, pp. 27–38. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  27. 27.
    Pettré, J., Laumond, J.P., Siméon, T.: A 2-stages locomotion planner for digital actors. In: Proc. of the 2003 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, SCA 2003, pp. 258–264 (2003)Google Scholar
  28. 28.
    Reynolds, C.W.: Flocks, herds and schools: A distributed behavioral model. SIGGRAPH Comput. Graph. 21, 25–34 (1987)CrossRefGoogle Scholar
  29. 29.
    Shao, W., Terzopoulos, D.: Autonomous pedestrians. Graphical Models 69(5-6), 246–274 (2007); special Issue on SCA 2005Google Scholar
  30. 30.
    Stone, M., DeCarlo, D., Oh, I., Rodriguez, C., Stere, A., Lees, A., Bregler, C.: Speaking with hands: creating animated conversational characters from recordings of human performance. ACM Trans. Graph. 23(3), 506–513 (2004)CrossRefGoogle Scholar
  31. 31.
    Thomas, G., Donikian, S.: Virtual humans animation in informed urban environments. In: Proc. of the Computer Animation, CA 2000, pp. 112–119. IEEE Computer Society (2000)Google Scholar
  32. 32.
    Tsai, J., Fridman, N., Bowring, E., Brown, M., Epstein, S., Kaminka, G., Marsella, S., Ogden, A., Rika, I., Sheel, A., Taylor, M.E., Wang, X., Zilka, A., Tambe, M.: Escapes: evacuation simulation with children, authorities, parents, emotions, and social comparison. In: Proc. of the 10th Intl. Conf. on Autonomous Agents and Multiagent Systems, AAMAS 2011, vol. 2, pp. 457–464 (2011)Google Scholar
  33. 33.
    Wampler, K., Andersen, E., Herbst, E., Lee, Y., Popović, Z.: Character animation in two-player adversarial games. ACM Trans. Graph. 29(3), 26:1–26:13 (2010)Google Scholar
  34. 34.
    Yu, Q., Terzopoulos, D.: A decision network framework for the behavioral animation of virtual humans. In: Proc. of the 2007 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, SCA 2007, pp. 119–128 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Seung In Park
    • 1
  • Chao Peng
    • 1
  • Francis Quek
    • 1
  • Yong Cao
    • 1
  1. 1.Department of Computer ScienceVirginia TechUSA

Personalised recommendations