Abstract
In this chapter, we propose a novel approach in which a system autonomously composes dance sequences from previously taught dance moves with the help of the well-known differential evolution algorithm. In this chapter, we propose a novel approach in which a system autonomously composes dance sequences from previously taught dance moves with the help of the well-known differential evolution algorithm. Initially, we generated a large population of dance sequences. The fitness of each of these sequences was determined by calculating the total inter-move transition abruptness of the adjacent dance moves. The transition abruptness was calculated as the difference of corresponding slopes formed by connected body joint co-ordinates. By visually evaluating the dance sequences created, it was observed that the fittest dance sequence had the least abrupt inter-move transitions. Computer simulation undertaken revealed that the developed dance video frames do not have significant inter-move transition abruptness between two successive frames, indicating the efficacy of the proposed approach. Gestural data specific of dance moves is captured using a Microsoft Kinect sensor. The algorithm developed by us was used to fuse the dancing styles of various ‘Odissi’ dancers dancing to the same rasa (theme) and tala (beats) and loy (rhythm). In future, it may be used to fuse different forms of dance.
Contributed by Reshma Kar, Amit Konar and Aruna Chakraborty
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
A. Soga, B. Umino, M. Hirayama, Automatic composition for contemporary dance using 3D motion clips: Experiment on dance training and system evaluation, in CW’09. International Conference on CyberWorlds, 2009, pp. 171–176
J. Dancs, R. Sivalingam, G. Somasundaram, V. Morellas, N. Papanikolopoulos, Recognition of ballet micro-movements for use in choreography, in IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013, pp. 1162–1167
S. Colton, R. de López Mantaras, O. Stock, Computational Creativity: Coming of Age. AI Mag. 30(3), 11 (2009)
G. Widmer, S. Flossmann, M. Grachten, YQX plays Chopin. AI Mag. 30(3), 35 (2009)
P. Gervás, Computational approaches to storytelling and creativity. AI Mag. 30(3), 49 (2009)
M. Edwards, Algorithmic composition: computational thinking in music. Commun. ACM 54(7), 58–67 (2011)
S.F. de Sousa Junior, M.F.M. Campos, Shall we dance? A music-driven approach for mobile robots choreography, in IEEE/RSJ International Conference on Intelligent Robots and Systems, 2011, pp. 1974–1979
S. Jadhav, M. Joshi, J. Pawar, Art to SMart: an evolutionary computational model for BharataNatyam choreography, in 12th International Conference on Hybrid Intelligent Systems (HIS), 2012, pp. 384–389
A. Soga, M. Endo, T. Yasuda, Motion description and composing system for classic ballet animation on the Web, in Proceedings of 10th IEEE International Workshop on Robot and Human Interactive Communication, 2001, pp. 134–139
R. Storn, K. Price, Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)
R. Storn, K. Price, Differential Evolution—A Simple and Efficient Adaptive Scheme for Global Optimization Over Continuous Spaces. ICSI Berkeley, 1995
K. Price, R. M. Storn, J. A. Lampinen, Differential Evolution: A Practical Approach to Global Optimization (Springer, Berlin, 2006)
J. Vesterstrom, R. Thomsen, A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems, in CEC2004. Congress on Evolutionary Computation, 2004, vol. 2, pp. 1980–1987
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this chapter
Cite this chapter
Konar, A., Saha, S. (2018). Differential Evolution Based Dance Composition. In: Gesture Recognition. Studies in Computational Intelligence, vol 724. Springer, Cham. https://doi.org/10.1007/978-3-319-62212-5_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-62212-5_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-62210-1
Online ISBN: 978-3-319-62212-5
eBook Packages: EngineeringEngineering (R0)