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Choreography Composed by Deep Learning

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HCI International 2021 - Posters (HCII 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1420))

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Abstract

Choreography is a type of art in which movement is designed. Because it involves many complex movements, a lot of time is spent creating the choreography. In this study, we constructed a dataset of dance movements with more than 700,000 frames using dance videos of the “Odotte-mita” genre. Our dataset could have been very useful for recent research in motion generation, as there has been a lack of dance datasets. Furthermore, we verified the choreography of the dance generated by the deep learning method (acRNN) by physically repeating the dance steps. To the best of our knowledge, this is the first time such a verification has been attempted. It became clear that the choreography generated by machine learning had unique movements and rhythms for dancers. In addition, dancing machine-learning-generated choreography provided an opportunity to make new discoveries. In addition, the fact that the model was a stick figure made the choreography vague, and, although it was difficult to remember, it created varied dance steps.

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Correspondence to Ryosuke Suzuki .

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Suzuki, R., Ochiai, Y. (2021). Choreography Composed by Deep Learning. In: Stephanidis, C., Antona, M., Ntoa, S. (eds) HCI International 2021 - Posters. HCII 2021. Communications in Computer and Information Science, vol 1420. Springer, Cham. https://doi.org/10.1007/978-3-030-78642-7_73

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  • DOI: https://doi.org/10.1007/978-3-030-78642-7_73

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78641-0

  • Online ISBN: 978-3-030-78642-7

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