Dance Dance Gradation: A Generation of Fine-Tuned Dance Charts - Archive ouverte HAL Access content directly
Conference Papers Year : 2018

Dance Dance Gradation: A Generation of Fine-Tuned Dance Charts

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Yudai Tsujino
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  • PersonId : 1047366
Ryosuke Yamanishi
  • Function : Author
  • PersonId : 1047367

Abstract

This paper proposes a system to automatically generate dance charts with fine-tuned difficulty levels: Dance Dance Gradation (DDG). The system learns the relationships between difficult and easy charts based on the deep neural network using a dataset of dance charts with different difficulty levels as the training data. The difficulty chart automatically would be adapted to easier charts through the learned model. As mixing multiple difficulty levels for the training data, the generated charts should have each characteristic of difficulty level. The user can obtain the charts with intermediate difficulty level between two different levels. Through the objective evaluation and the discussions for the output results, it was suggested that the proposed system generated the charts with each characteristic of the difficulty level in the training dataset.
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Dates and versions

hal-02128628 , version 1 (14-05-2019)

Licence

Attribution - CC BY 4.0

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Yudai Tsujino, Ryosuke Yamanishi. Dance Dance Gradation: A Generation of Fine-Tuned Dance Charts. 17th International Conference on Entertainment Computing (ICEC), Sep 2018, Poznan, Poland. pp.175-187, ⟨10.1007/978-3-319-99426-0_15⟩. ⟨hal-02128628⟩
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