Skip to main content
SpringerLink
Log in

Expressive Aliens - Laban Effort Factors for Non-anthropomorphic Morphologies

  • Conference paper
  • First Online:
Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13221))

  • 1813 Accesses

Abstract

The use of computer generated characters as artificial dancers offers interesting creative possibilities, especially when endowing these characters with morphologies and behaviours that differ significantly from those of human dancers. At the same time, it is challenging to create movements for non-anthropomorphic characters that are at the same time expressive and physically plausible. Motion synthesis techniques based on data driven methods or physics simulation each have their own limitations concerning the aspects of movements and the range of morphologies they can be used for. This paper presents a proof of concept system that combines a data driven method with a physics simulation for synthesizing expressive movements for computer generated characters with arbitrary morphologies. A core component of the system is a reinforcement learning algorithm that employs reward functions based on Laban Effort Factors. This system has been tested by training three different non-anthropomorphic morphologies on different combinations of these reward functions. The results obtained so far indicate that the system is able to generate a large diversity of poses and motions which reflect the characteristics of each morphology and Effort Factor.

Supported by the H2020-MSCA-IF-2018 - GA No. 840465.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
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

Notes

  1. 1.

    PyTorch pytorch.org.

  2. 2.

    OpenAI Spinning Up github.com/openai/spinningup.

  3. 3.

    PyBullet pybullet.org.

  4. 4.

    PyBullet Gymperium github.com/benelot/pybullet-gym.

  5. 5.

    onShape www.onshape.com.

  6. 6.

    Biped Animations: D0, D0F0, D0F1, D0S0, D0S1, D0T0, D0T1, D0W0, D0W1, D1, D1F0, D1F1, D1S0, D1S1, D1T0, D1T1, D1W0, D1W1, Quadruped Animations: D0, D0F0, D0F1, D0S0, D0S1, D0T0, D0T1, D0W0, D0W1, D1, D1F0, D1F1, D1S0, D1S1, D1T0, D1T1, D1W0, D1W1, Legless Animations: D0, D0F0, D0F1, D0S0, D0S1, D0T0, D0T1, D0W0, D0W1, D1, D1F0, D1F1, D1S0, D1S1, D1T0, D1T1, D1W0, D1W1.

References

  1. Ajili, I., Mallem, M., Didier, J.Y.: Human motions and emotions recognition inspired by LMA qualities. Vis. Comput. 35(10), 1411–1426 (2019). https://doi.org/10.1007/s00371-018-01619-w

    Article  Google Scholar 

  2. Alemi, O., Pasquier, P.: Machine learning for data-driven movement generation: a review of the state of the art. arXiv preprint arXiv:1903.08356 (2019)

  3. Aristidou, A., Charalambous, P., Chrysanthou, Y.: Emotion analysis and classification: understanding the performers’ emotions using the LMA entities. In: Computer Graphics Forum, vol. 34, pp. 262–276. Wiley (2015)

    Google Scholar 

  4. Aristidou, A., Stavrakis, E., Charalambous, P., Chrysanthou, Y., Himona, S.L.: Folk dance evaluation using Laban movement analysis. J. Comput. Cult. Herit. (JOCCH) 8(4), 1–19 (2015)

    Article  Google Scholar 

  5. Bartenieff, I., Lewis, D.: Body Movement: Coping with the Environment, 1st edn. Routledge, London (2013)

    Book  Google Scholar 

  6. Berman, A., James, V.: Towards a live dance improvisation between an avatar and a human dancer. In: Proceedings of the 2014 International Workshop on Movement and Computing, pp. 162–165 (2014)

    Google Scholar 

  7. Berman, A., James, V.: Kinetic dialogues: enhancing creativity in dance. In: Proceedings of the 2nd International Workshop on Movement and Computing, pp. 80–83 (2015)

    Google Scholar 

  8. Berman, A., James, V.: Learning as performance: autoencoding and generating dance movements in real time. In: Liapis, A., Romero Cardalda, J.J., Ekárt, A. (eds.) EvoMUSART 2018. LNCS, vol. 10783, pp. 256–266. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-77583-8_17

    Chapter  Google Scholar 

  9. Bisig, D., Palacio, P.: Neural narratives: dance with virtual body extensions. In: Proceedings of the 3rd International Symposium on Movement and Computing, pp. 1–8 (2016)

    Google Scholar 

  10. Camurri, A., et al.: EyesWeb: toward gesture and affect recognition in interactive dance and music systems. Comput. Music. J. 24(1), 57–69 (2000)

    Article  Google Scholar 

  11. Camurri, A., Mazzarino, B., Ricchetti, M., Timmers, R., Volpe, G.: Multimodal analysis of expressive gesture in music and dance performances. In: Camurri, A., Volpe, G. (eds.) GW 2003. LNCS (LNAI), vol. 2915, pp. 20–39. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24598-8_3

    Chapter  Google Scholar 

  12. Carlson, K., Pasquier, P., Tsang, H.H., Phillips, J., Schiphorst, T., Calvert, T.: Cochoreo: a generative feature in idanceForms for creating novel keyframe animation for choreography. In: Proceedings of the Seventh International Conference on Computational Creativity (2016)

    Google Scholar 

  13. Carlson, K., Schiphorst, T., Pasquier, P.: Scuddle: generating movement catalysts for computer-aided choreography. In: ICCC, pp. 123–128 (2011)

    Google Scholar 

  14. Crnkovic-Friis, L., Crnkovic-Friis, L.: Generative choreography using deep learning. arXiv preprint arXiv:1605.06921 (2016)

  15. Dautenhahn, K.: Human-robot interaction. In: The Encyclopedia of Human-Computer Interaction, 2nd edn. (2013)

    Google Scholar 

  16. DeLahunta, S.: The choreographic language agent. In: Conference Proceedings of the 2008 World Dance Alliance Global Summit (2009)

    Google Scholar 

  17. Demers, L.P.: The multiple bodies of a machine performer. In: Robots and Art: Exploring an Unlikely Symbiosis, pp. 273–306. Springer, Singapore (2016). https://doi.org/10.1007/978-981-10-0321-9_14

  18. Duffy, B.R.: Anthropomorphism and the social robot. Robot. Auton. Syst. 42(3–4), 177–190 (2003)

    Article  Google Scholar 

  19. Fdili Alaoui, S., Henry, C., Jacquemin, C.: Physical modelling for interactive installations and the performing arts. Int. J. Perform. Arts Digit. Media 10(2), 159–178 (2014)

    Article  Google Scholar 

  20. Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft actor-critic: off-policy maximum entropy deep reinforcement learning with a stochastic actor. In: International Conference on Machine Learning, pp. 1861–1870. PMLR (2018)

    Google Scholar 

  21. Hsieh, C.M., Luciani, A.: Generating dance verbs and assisting computer choreography. In: Proceedings of the 13th Annual ACM International Conference on Multimedia, pp. 774–782 (2005)

    Google Scholar 

  22. Hsueh, S., Alaoui, S.F., Mackay, W.E.: Understanding kinaesthetic creativity in dance. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pp. 1–12 (2019)

    Google Scholar 

  23. Jacob, M., Magerko, B.: Interaction-based authoring for scalable co-creative agents. In: ICCC, pp. 236–243 (2015)

    Google Scholar 

  24. Jochum, E., Derks, J.: Tonight we improvise! Real-time tracking for human-robot improvisational dance. In: Proceedings of the 6th International Conference on Movement and Computing, pp. 1–11 (2019)

    Google Scholar 

  25. Joshi, M., Chakrabarty, S.: An extensive review of computational dance automation techniques and applications. Proc. R. Soc. A 477(2251), 20210071 (2021)

    Article  Google Scholar 

  26. Kaspersen, E.T., Górny, D., Erkut, C., Palamas, G.: Generative choreographies: the performance dramaturgy of the machine. In: Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications-Volume 1: GRAPP, pp. 319–326. SCITEPRESS Digital Library (2020)

    Google Scholar 

  27. Lapointe, F.J., Époque, M.: The dancing genome project: generation of a human-computer choreography using a genetic algorithm. In: Proceedings of the 13th Annual ACM International Conference on Multimedia, pp. 555–558 (2005)

    Google Scholar 

  28. Larboulette, C., Gibet, S.: A review of computable expressive descriptors of human motion. In: Proceedings of the 2nd International Workshop on Movement and Computing, MOCO 2015, pp. 21–28 (2015)

    Google Scholar 

  29. Leach, J., Delahunta, S.: Dance becoming knowledge: designing a digital “body’’. Leonardo 50(5), 461–467 (2017)

    Article  Google Scholar 

  30. Liu, L., Long, D., Gujrania, S., Magerko, B.: Learning movement through human-computer co-creative improvisation. In: Proceedings of the 6th International Conference on Movement and Computing, pp. 1–8 (2019)

    Google Scholar 

  31. Lubart, T.: How can computers be partners in the creative process: classification and commentary on the special issue. Int. J. Hum. Comput. Stud. 63(4–5), 365–369 (2005)

    Article  Google Scholar 

  32. Maletic, V.: Body, Space, Expression: The Development of Rudolf Laban’s Movement and Dance Concepts. Approaches to Semiotics, Berlin (1987)

    Google Scholar 

  33. McCormick, J., Hutchinson, S., Vincs, K., Vincent, J.B.: Emergent behaviour: learning from an artificially intelligent performing software agent. In: ISEA 2015: Proceedings of the 21st International Symposium on Electronic Art, pp. 1–4 (2015)

    Google Scholar 

  34. McCormick, J., Vincs, K., Nahavandi, S., Creighton, D.: Learning to dance with a human. ISEA International, Australian Network for Art & Technology, University of Sydney, January 2013

    Google Scholar 

  35. McCormick, J., Vincs, K., Nahavandi, S., Creighton, D., Hutchison, S.: Teaching a digital performing agent: artificial neural network and hidden Markov model for recognising and performing dance movement. In: Proceedings of the 2014 International Workshop on Movement and Computing, pp. 70–75 (2014)

    Google Scholar 

  36. Mourot, L., Hoyet, L., Le Clerc, F., Schnitzler, F., Hellier, P.: A survey on deep learning for skeleton-based human animation. In: Computer Graphics Forum. Wiley (2021)

    Google Scholar 

  37. Von Laban, R.: Modern Educational Dance, 3rd edn. McDonald & Evans, London (1975)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Center for Dance Research, Coventry University, Coventry, UK

    Daniel Bisig

  2. Institute for Computer Music and Sound Technology, Zurich University of the Arts, Zurich, Switzerland

    Daniel Bisig

Authors
  1. Daniel Bisig
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daniel Bisig .

Editor information

Editors and Affiliations

  1. University of Coimbra, Coimbra, Portugal

    Tiago Martins

  2. University of A Coruña, A Coruña, Spain

    Nereida Rodríguez-Fernández

  3. University of Coimbra, Coimbra, Portugal

    Sérgio M. Rebelo

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bisig, D. (2022). Expressive Aliens - Laban Effort Factors for Non-anthropomorphic Morphologies. In: Martins, T., Rodríguez-Fernández, N., Rebelo, S.M. (eds) Artificial Intelligence in Music, Sound, Art and Design. EvoMUSART 2022. Lecture Notes in Computer Science, vol 13221. Springer, Cham. https://doi.org/10.1007/978-3-031-03789-4_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-03789-4_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-03788-7

  • Online ISBN: 978-3-031-03789-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation