Towards Bi-directional Dancing Interaction

  • Dennis Reidsma
  • Herwin van Welbergen
  • Ronald Poppe
  • Pieter Bos
  • Anton Nijholt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4161)

Abstract

Dancing is an entertaining form of taskless interaction. When interacting with a dancing Embodied Conversational Agent (ECA), the lack of a clear task presents the challenge of eliciting an interaction between user and ECA in a different way. In this paper we describe our Virtual Dancer, which is an ECA that invites a user to dance. In our system the user is monitored using global movement characteristics from a camera and a dance pad. The characteristics are used to select and adapt movements for the Virtual Dancer. This way, the user can dance together with the Virtual Dancer. Any interaction patterns and implicit relations between the dance behaviour of the human and the Virtual Dancer should be evoked intuitively without explicit appeal. The work described in this paper can be used as a platform for research into natural animation and user invitation behavior. We discuss future work on both topics.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Perlin, K.: Real time responsive animation with personality. IEEE Transactions on Visualization and Computer Graphics 1(1), 5–15 (1995)CrossRefGoogle Scholar
  2. 2.
    Mataric, M., Zordan, V., Williamson, M.: Making complex articulated agents dance. Autonomous Agents and Multi-Agent Systems 2(1), 23–43 (1999)CrossRefGoogle Scholar
  3. 3.
    Shiratori, T., Nakazawa, A., Ikeuchi, K.: Rhythmic motion analysis using motion capture and musical information. In: Proceedings of the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, pp. 89–94 (2003)Google Scholar
  4. 4.
    Nakazawa, A., Nakaoka, S., Kudoh, S., Ikeuchi, K.: Digital archive of human dance motions. In: Proceedings of the International Conference on Virtual Systems and Multimedia (2002)Google Scholar
  5. 5.
    Kim, T., Park, S.I., Shin, S.Y.: Rhythmic-motion synthesis based on motion-beat analysis. ACM Transactions on Graphics 22(3), 392–401 (2003)CrossRefGoogle Scholar
  6. 6.
    Chen, J., Li, T.: Rhythmic character animation: Interactive chinese lion dance. In: Proceedings of the International Conference on Computer Animation and Social Agents (2005)Google Scholar
  7. 7.
    Ren, L., Shakhnarovich, G., Hodgins, J.K., Pfister, H., Viola, P.: Learning silhouette features for control of human motion. ACM Transcactions on Graphics 24(4), 1303–1331 (2005)CrossRefGoogle Scholar
  8. 8.
    Kosuge, K., Hayashi, T., Hirata, Y., Tobiyama, R.: Dance partner robot –ms dancer–. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3459–3464 (2003)Google Scholar
  9. 9.
    Gouyon, F., Klapuri, A., Dixon, S., Alonso, M., Tzanetakis, G., Uhle, C., Cano, P.: An experimental comparison of audio tempo induction algorithms. IEEE Transactions on Speech and Audio Processing (in press, 2006)Google Scholar
  10. 10.
    Klapuri, A., Eronen, A., Astola, J.: Analysis of the meter of acoustic musical signals. IEEE transactions on Speech and Audio Processing 14(1), 342–355 (2006)CrossRefGoogle Scholar
  11. 11.
    Scheirer, E.D.: Tempo and beat analysis of acoustic musical signals. Journal of the Acoustical Society of America 103(1), 558–601 (1998)CrossRefGoogle Scholar
  12. 12.
    Kuper, J., Saggion, H., Cunningham, H., Declerck, T., de Jong, F., Reidsma, D., Wilks, Y., Wittenburg, P.: Intelligent multimedia indexing and retrieval through multi-source information extraction and merging. In: International Joint Conference of Artificial Intelligence, Acapulco, Mexico, February 2003, pp. 409–414 (2003)Google Scholar
  13. 13.
    Meredith, M., Maddock, S.: Using a half-jacobian for real-time inverse kinematics. In: Proceedings of the International Conference on Computer Games: Artificial Intelligence, Design and Education, pp. 81–88 (2004)Google Scholar
  14. 14.
    Meredith, M., Maddock, S.: Adapting motion capture using weighted real-time inverse kinematics. ACM Computers in Entertainment 3(1) (2005) (This is a Web-based journal)Google Scholar
  15. 15.
    Kovar, L., Gleicher, M., Pighin, F.H.: Motion graphs. ACM Trans. Graph. 21(3), 473–482 (2002)CrossRefGoogle Scholar
  16. 16.
    Pullen, K., Bregler, C.: Motion capture assisted animation: texturing and synthesis. In: Proceedings of the annual conference on Computer graphics and interactive techniques, pp. 501–508 (2002)Google Scholar
  17. 17.
    Safonova, A., Hodgins, J.K., Pollard, N.S.: Synthesizing physically realistic human motion in low-dimensional, behavior-specific spaces. ACM Trans. Graph. 23(3), 514–521 (2004)CrossRefGoogle Scholar
  18. 18.
    Liu, K.C., Hertzmann, A., Popovic, Z.: Learning physics-based motion style with nonlinear inverse optimization. ACM Trans. Graph. 24(3), 1071–1081 (2005)CrossRefGoogle Scholar
  19. 19.
    Boker, S., Rotondo, J.: In: Symmetry building and symmetry breaking in synchronized movement, pp. 163–171 (2003)Google Scholar
  20. 20.
    Peters, C.: Direction of attention perception for conversation initiation in virtual environments. In: Proceedings of the Intelligent Virtual Agents, International Working Conference, pp. 215–228. Springer, Heidelberg (2005)Google Scholar
  21. 21.
    Shell, J., Selker, T., Vertegaal, R.: Interacting with groups of computers. Special Issue on Attentive User Interfaces. Communications of ACM 46(3), 40–46 (2003)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2006

Authors and Affiliations

  • Dennis Reidsma
    • 1
  • Herwin van Welbergen
    • 1
  • Ronald Poppe
    • 1
  • Pieter Bos
    • 1
  • Anton Nijholt
    • 1
  1. 1.Human Media Interaction GroupUniversity of TwenteEnschedeThe Netherlands

Personalised recommendations