Abstract
With recent developments in artificial intelligence, it is possible to generate human motion using deep learning. In this paper, a transformer deep learning algorithm is investigated to generate improvisation dance motions for the Another Kind of Blue (AKOB) data set. AKOB is an innovative dance group, located in The Hague, Netherlands, with a specialization in combining modern dance and technology. For this study, AKOB recorded various dance movements with different pieces of music using a motion capture system. This data is used to train a transformer network and generate sequences of improvisational dance using seed motions and musical input. The produced movements are visualized and compared to the ground truth of human motions to examine their quality. The results show possible human positions, but the speed of motions is a lot compared to the music. Also, sometimes the transition from one position to another is not feasible.
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Acknowledgements
We would like to express our gratitude to David Middendorp and the Another Kind of Blue crew for their participation in this project. We would also like to thank Bas van der Linden for his technical advice.
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© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Asadi, Z., Moons, J., Leijnen, S. (2023). Next Level Choreography: Applying a Transformer Network to Generate Improvised Dance Motions. In: Brooks, A.L. (eds) ArtsIT, Interactivity and Game Creation. ArtsIT 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 479. Springer, Cham. https://doi.org/10.1007/978-3-031-28993-4_36
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DOI: https://doi.org/10.1007/978-3-031-28993-4_36
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