Style-based motion analysis for dance composition

  • Andreas Aristidou
  • Efstathios Stavrakis
  • Margarita Papaefthimiou
  • George Papagiannakis
  • Yiorgos Chrysanthou
Original Article
First Online:
  • 113 Downloads

Abstract

Synthesizing human motions from existing motion capture data is the approach of choice in most applications requiring high- quality visual results. Usually to synthesize motion, short motion segments are concatenated into longer sequences by finding transitions at points where character poses are similar. If similarity is only a measure of posture correlation, without consideration for the stylistic variations of movement, the resulting motion might have unnatural discontinuities. Particularly prone to this problem are highly stylized motions, such as dance performances. This work presents a motion analysis framework, based on Laban Movement Analysis, that also accounts for stylistic variations of the movement. Implemented in the context of Motion Graphs, it is used to eliminate potentially problematic transitions and synthesize style-coherent animation, without requiring prior labeling of the data. The effectiveness of our method is demonstrated by synthesizing contemporary dance performances that include a variety of different emotional states. The algorithm is able to compose highly stylized motions that are reminiscent to dancing scenarios using only plausible movements from existing clips.

Keywords

Laban Movement Analysis Motion Graphs Motion style Motion synthesis 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgements

This work is co-financed by the European Regional Development Fund and the Republic of Cyprus through the Research Promotion Foundation Under Contract DIDAKTOR/0311/73

Supplementary material

Supplementary material 1 (mp4 16901 KB)

References

  1. 1.
    Alexiadis, D.S., Daras, P.: Quaternionic signal processing techniques for automatic evaluation of dance performances from mocap data. IEEE Trans. Multimedia 99, 1–16 (2014)Google Scholar
  2. 2.
    Arikan, O., Forsyth, D.A.: Interactive motion generation from examples. ACM Trans. Gr. 21(3), 483–490 (2002)CrossRefMATHGoogle Scholar
  3. 3.
    Aristidou, A., Charalambous, P., Chrysanthou, Y.: Emotion analysis and classification: understanding the performers emotions using the LMA entities. Comput. Gr. Forum 34(6), 262–276 (2015)CrossRefGoogle Scholar
  4. 4.
    Aristidou, A., Stavrakis, E., Charalambous, P., Chrysanthou, Y., Himona, S.L.: Folk dance evaluation using laban movement analysis. ACM J. Comput. Cult. Herit. 8(4), 20:1–20:19 (2015)Google Scholar
  5. 5.
    Aristidou, A., Zeng, Q., Stavrakis, E., Yin, K., Cohen-or, D., Chrysanthou, Y., Chen, B.: Emotion control of unstructured dance movements. In: Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation, SCA ’17. Eurographics Association, Aire-la-Ville, Switzerland (2017)Google Scholar
  6. 6.
    Brand, M., Hertzmann, A.: Style machines. In: Proceedings of SIGGRAPH ’00, pp. 183–192. ACM Press/Addison-Wesley Publishing Co., New York (2000)Google Scholar
  7. 7.
    Calvert, T., Wilke, W., Ryman, R., Fox, I.: Applications of computers to dance. IEEE Comput. Gr. Appl. 25(2), 6–12 (2005)CrossRefGoogle Scholar
  8. 8.
    Chan, J., Leung, H., Tang, J., Komura, T.: A virtual reality dance training system using motion capture technology. IEEE Trans. Learn. Technol. 4(2), 187–195 (2011)CrossRefGoogle Scholar
  9. 9.
    Chao, M.W., Lin, C.H., Assa, J., Lee, T.Y.: Human motion retrieval from hand-drawn sketch. IEEE Trans. Visual. Comput. Gr. 18(5), 729–740 (2012)CrossRefGoogle Scholar
  10. 10.
    Chi, D., Costa, M., Zhao, L., Badler, N.: The emote model for effort and shape. In: Proceedings of SIGGRAPH ’00, pp. 173–182. ACM, New York (2000)Google Scholar
  11. 11.
    Durupinar, F., Kapadia, M., Deutsch, S., Neff, M., Badler, N.I.: PERFORM: perceptual approach for adding OCEAN personality to human motion using laban movement analysis. ACM Trans. Gr. 36(1), 6:1–6:16 (2016)CrossRefGoogle Scholar
  12. 12.
    Hartmann, B., Mancini, M., Pelachaud, C.: Formational parameters and adaptive prototype instantiation for mpeg-4 compliant gesture synthesis. In: Proceedings of the Computer Animation, CA ’02, pp. 111–120. IEEE Computer Society, Washington, DC, USA (2002)Google Scholar
  13. 13.
    Hartmann, B., Mancini, M., Pelachaud, C.: Implementing expressive gesture synthesis for embodied conversational agents. In: Proceedings of GW ’05, pp. 188–199. Springer, Berlin (2006)Google Scholar
  14. 14.
    Hartmann, S., Trunz, E., Krüger, B., Klein, R., Hullin, M.B.: Efficient multi-constrained optimization for example-based synthesis. Vis. Comput./Proc. Comput. Gr. Int. (CGI 2015) 31(6–8), 893–904 (2015)Google Scholar
  15. 15.
    Holden, D., Saito, J., Komura, T.: A deep learning framework for character motion synthesis and editing. ACM Trans. Gr. 35(4), 138:1–138:11 (2016)CrossRefGoogle Scholar
  16. 16.
    Hsu, E., Pulli, K., Popović, J.: Style translation for human motion. ACM Trans. Gr. 24(3), 1082–1089 (2005)CrossRefGoogle Scholar
  17. 17.
    Kapadia, M., Chiang, I.k., Thomas, T., Badler, N.I., Kider Jr., J.T.: Efficient motion retrieval in large motion databases. In: Proceedings of I3D ’13, pp. 19–28. ACM, New York (2013)Google Scholar
  18. 18.
    Kovar, L., Gleicher, M.: Automated extraction and parameterization of motions in large data sets. ACM Trans. Gr. 23(3), 559–568 (2004)CrossRefGoogle Scholar
  19. 19.
    Kovar, L., Gleicher, M., Pighin, F.: Motion graphs. ACM Trans. Gr. 21(3), 473–482 (2002)CrossRefGoogle Scholar
  20. 20.
    Krüger, B., Tautges, J., Weber, A., Zinke, A.: Fast local and global similarity searches in large motion capture databases. In: Proceedings of SCA ’10, pp. 1–10 (2010)Google Scholar
  21. 21.
    Laban, R., Ullmann, L.: The Mastery of Movement, 4th edn. Dance Books Ltd, Binsted (2011)Google Scholar
  22. 22.
    Lee, J., Chai, J., Reitsma, P.S.A., Hodgins, J.K., Pollard, N.S.: Interactive control of avatars animated with human motion data. ACM Trans. Gr. 21(3), 491–500 (2002)Google Scholar
  23. 23.
    Li, Y., Wang, T., Shum, H.Y.: Motion texture: a two-level statistical model for character motion synthesis. ACM Trans. Gr. 21(3), 465–472 (2002)CrossRefGoogle Scholar
  24. 24.
    Luo, P., Neff, M.: A perceptual study of the relationship between posture and gesture for virtual characters. In: Motion in Games, pp. 254–265 (2012)Google Scholar
  25. 25.
    Min, J., Chai, J.: Motion graphs++: a compact generative model for semantic motion analysis and synthesis. ACM Trans. Gr. 31(6), 153:1–153:12 (2012)CrossRefGoogle Scholar
  26. 26.
    Min, J., Liu, H., Chai, J.: Synthesis and editing of personalized stylistic human motion. In: Proceedings of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, I3D ’10, pp. 39–46. ACM, NY (2010)Google Scholar
  27. 27.
    Müller, M., Röder, T.: Motion templates for automatic classification and retrieval of motion capture data. In: Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation, SCA ’06, 137–146 (2006)Google Scholar
  28. 28.
    Müller, M., Röder, T., Clausen, M.: Efficient content-based retrieval of motion capture data. ACM Trans. Gr. 24(3), 677–685 (2005)CrossRefGoogle Scholar
  29. 29.
    Nakata, T., Mori, T., Sato, T.: Analysis of impression of robot bodily expression. J. Robot. Mechatron. 14(1), 27–36 (2002)CrossRefGoogle Scholar
  30. 30.
    Okajima, S., Wakayama, Y., Okada, Y.: Human motion retrieval system based on LMA features using IEC method. In: Innovations in Intelligent Machines, pp. 117–130 (2012)Google Scholar
  31. 31.
    Oliveira, J.L., Naveda, L.A., Gouyon, F., Leman, M., Reis, L.P.: Synthesis of variable dancing styles based on a compact spatiotemporal representation of dance. In: Proceedings of IROS ’10 (2010)Google Scholar
  32. 32.
    Pearson, K.: Notes on the history of correlation. Biometrika 13(1), 25–45 (1920)CrossRefGoogle Scholar
  33. 33.
    Pejsa, T., Pandzic, I.S.: State of the art in example-based motion synthesis for virtual characters in interactive applications. Comput. Gr. Forum 29(1), 202–226 (2010)CrossRefGoogle Scholar
  34. 34.
    Ren, L., Patrick, A., Efros, A.A., Hodgins, J.K., Rehg, J.M.: A data-driven approach to quantifying natural human motion. ACM Trans. Gr. 24(3), 1090–1097 (2005)CrossRefGoogle Scholar
  35. 35.
    Russell, J.A.: A circumplex model of affect. J. Personal. Soc. Psychol. 39, 1161–1178 (1980)CrossRefGoogle Scholar
  36. 36.
    Senecal, S., Cuel, L., Aristidou, A., Magnenat-Thalman, N.: Continuous body emotion recognition system during theater performances. Comput. Anim. Virtual Worlds 27(3–4), 311–320 (2016)CrossRefGoogle Scholar
  37. 37.
    Shapiro, A., Cao, Y., Faloutsos, P.: Style components. In: Proceedings of Graphics Interface, GI ’06, pp. 33–39. Canadian Information Processing Society, Toronto, Canada (2006)Google Scholar
  38. 38.
    Shiratori, T., Nakazawa, A., Ikeuchi, K.: Dancing-to-music character animation. Comput. Gr. Forum 25(3), 449–458 (2006)CrossRefGoogle Scholar
  39. 39.
    Tang, J.K.T., Chan, J.C.P., Leung, H.: Interactive dancing game with real-time recognition of continuous dance moves from 3d human motion capture. In: Proceedings of ICUIMC ’11, pp. 50:1–50:9. ACM, New York (2011)Google Scholar
  40. 40.
    Torresani, L., Hackney, P., Bregler, C.: Learning motion style synthesis from perceptual observations. In: Proceedings of NIPS’06, pp. 1393–1400 (2006)Google Scholar
  41. 41.
    Urtasun, R., Glardon, P., Boulic, R., Thalmann, D., Fua, P.: Style-based motion synthesis. Comput. Gr. Forum 23(4), 799–812 (2004)CrossRefGoogle Scholar
  42. 42.
    Vasilescu, M.A.O.: Human motion signatures: analysis, synthesis, recognition. In: Proceedings of the International Conference on Pattern Recognition, ICPR ’02, pp. 456–460. IEEE, Washington, DC (2002)Google Scholar
  43. 43.
    Wakayama, Y., Okajima, S., Takano, S., Okada, Y.: IEC-based motion retrieval system using laban movement analysis. In: Proceedings of KES’10, pp. 251–260 (2010)Google Scholar
  44. 44.
    Wilke, L., Calvert, T., Ryman, R., Fox, I.: From dance notation to human animation: the labandancer project: motion capture and retrieval. Comput. Anim. Virtual Worlds 16(3–4), 201–211 (2005)CrossRefGoogle Scholar
  45. 45.
    Xia, S., Wang, C., Chai, J., Hodgins, J.: Realtime style transfer for unlabeled heterogeneous human motion. ACM Trans. Gr. 34(4), 119:1–119:10 (2015)CrossRefGoogle Scholar
  46. 46.
    Yang, Y., Leung, H., Yue, L., Deng, L.: Generating a two-phase lesson for guiding beginners to learn basic dance movements. Comput. Educ. 61, 1–20 (2013)CrossRefGoogle Scholar
  47. 47.
    Yumer, M.E., Mitra, N.J.: Spectral style transfer for human motion between independent actions. ACM Trans. Graph. 35(4), 137 (2016)CrossRefGoogle Scholar
  48. 48.
    Zhao, L., Badler, N.I.: Acquiring and validating motion qualities from live limb gestures. Gr. Models 67(1), 1–16 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Andreas Aristidou
    • 1
    • 2
  • Efstathios Stavrakis
    • 1
  • Margarita Papaefthimiou
    • 3
  • George Papagiannakis
    • 4
  • Yiorgos Chrysanthou
    • 1
  1. 1.University of CyprusNicosiaCyprus
  2. 2.Interdisciplinary Center HerzliyaHerzliyaIsrael
  3. 3.Institute of Computer Science of the Foundation for Research and Technology HellasHeraklionGreece
  4. 4.University of CreteRethymnonGreece

Personalised recommendations