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.
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References
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)
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)
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)
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)
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)
Kingston, P., Egerstedt, M.: Motion preference learning. In: American Control Conference (ACC), pp. 3819–3824. IEEE (2011)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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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
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DOI: https://doi.org/10.1007/978-3-030-05204-1_58
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