Skip to main content
SpringerLink
Log in

Imitating Human Movement Using a Measure of Verticality to Animate Low Degree-of-Freedom Non-humanoid Virtual Characters

  • Conference paper
  • First Online:
Social Robotics (ICSR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11357))

Included in the following conference series:

Abstract

Imitating human motion on robotic platforms is a task which requires ignoring some information about the original human mover as robots have fewer degrees of freedom than a human. In an effort to generate low degree of freedom motion profiles based on human movement, this paper utilizes verticality, computed from motion capture data, to animate virtual characters. After creating correspondences between the verticality metrics and the movement of three and four degree of freedom virtual characters, lay users were asked whether the imitation of the characters’ movements was effective compared to pseudo-random motion profiles. The results showed a statistically significant preference for the verticality method for the higher DOF character and for the higher DOF character over the lower DOF character. Future work includes extending the verticality method to more virtual characters and developing other methodologies of motion generation for users to evaluate a more diverse set of motion profiles. This work can help create automated protocols for replicating human motion, and intent, on artificial systems.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

References

  1. Abdul-Massih, M., Yoo, I., Benes, B.: Motion style retargeting to characters with different morphologies. In: Computer Graphics Forum, vol. 36, pp. 86–99. Wiley Online Library (2017)

    Google Scholar 

  2. Arvind, D., Valtazanos, A.: Speckled tango dancers: Real-time motion capture of two-body interactions using on-body wireless sensor networks. In: Sixth International Workshop on Wearable and Implantable Body Sensor Networks, BSN 2009, pp. 312–317. IEEE (2009)

    Google Scholar 

  3. Ashenfelter, K.T., Boker, S.M., Waddell, J.R., Vitanov, N.: Spatiotemporal symmetry and multifractal structure of head movements during dyadic conversation. J. Exp. Psychol.: Hum. Percept. Perform. 35(4), 1072 (2009)

    Google Scholar 

  4. Baillieul, J., Özcimder, K.: The control theory of motion-based communication: problems in teaching robots to dance. In: American Control Conference (ACC), pp. 4319–4326. IEEE (2012)

    Google Scholar 

  5. Kaushik, R., Vidrin, I., LaViers, A.: Quantifying coordination in human dyads via a measure of verticality. In: Proceedings of the 5th International Conference on Movement and Computing, p. 19. ACM (2018)

    Google Scholar 

  6. Kingston, P., Egerstedt, M.: Motion preference learning. In: American Control Conference (ACC), pp. 3819–3824. IEEE (2011)

    Google Scholar 

  7. Liu, C., Ishi, C.T., Ishiguro, H., Hagita, N.: Generation of nodding, head tilting and eye gazing for human-robot dialogue interaction. In: 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 285–292. IEEE (2012)

    Google Scholar 

  8. Minato, T., Ishiguro, H.: Generating natural posture in an android by mapping human posture in three-dimensional position space. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007, pp. 609–616. IEEE (2007)

    Google Scholar 

  9. Ott, C., Lee, D., Nakamura, Y.: Motion capture based human motion recognition and imitation by direct marker control. In: 8th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2008, pp. 399–405. IEEE (2008)

    Google Scholar 

  10. Özcimder, K., Dey, B., Lazier, R.J., Trueman, D., Leonard, N.E.: Investigating group behavior in dance: an evolutionary dynamics approach. In: American Control Conference (ACC), pp. 6465–6470. IEEE (2016)

    Google Scholar 

  11. Seol, Y., O’Sullivan, C., Lee, J.: Creature features: online motion puppetry for non-human characters. In: Proceedings of the 12th ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pp. 213–221. ACM (2013)

    Google Scholar 

  12. Shiratori, T., Nakazawa, A., Ikeuchi, K.: Synthesizing dance performance using musical and motion features. In: Proceedings 2006 IEEE International Conference on Robotics and Automation, ICRA 2006, pp. 3654–3659. IEEE (2006)

    Google Scholar 

  13. Tang, J.K., Chan, J.C., Leung, H.: Interactive dancing game with real-time recognition of continuous dance moves from 3D human motion capture. In: Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication, p. 50. ACM (2011)

    Google Scholar 

  14. Wang, H., Kosuge, K.: Control of a robot dancer for enhancing haptic human-robot interaction in waltz. IEEE Trans. Haptics 5(3), 264–273 (2012)

    Article  Google Scholar 

  15. Yamane, K., Ariki, Y., Hodgins, J.: Animating non-humanoid characters with human motion data. In: Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pp. 169–178. Eurographics Association (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA

    Roshni Kaushik & Amy LaViers

Authors
  1. Roshni Kaushik
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amy LaViers
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Roshni Kaushik .

Editor information

Editors and Affiliations

  1. The National University of Singapore, Singapore, Singapore

    Shuzhi Sam Ge

  2. Qatar University, Doha, Qatar

    John-John Cabibihan

  3. University Carlos III de Madrid, Madrid, Spain

    Miguel A. Salichs

  4. Canterbury University, Auckland, New Zealand

    Elizabeth Broadbent

  5. Wichita State University, Wichita, KS, USA

    Hongsheng He

  6. The Pennsylvania State University, University Park, PA, USA

    Alan R. Wagner

  7. Universidad Carlos III de Madrid, Madrid, Spain

    Álvaro Castro-González

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kaushik, R., LaViers, A. (2018). Imitating Human Movement Using a Measure of Verticality to Animate Low Degree-of-Freedom Non-humanoid Virtual Characters. In: Ge, S., et al. Social Robotics. ICSR 2018. Lecture Notes in Computer Science(), vol 11357. Springer, Cham. https://doi.org/10.1007/978-3-030-05204-1_58

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-05204-1_58

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05203-4

  • Online ISBN: 978-3-030-05204-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation