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arXiv · 1703.06891

Dance Dance Convolution

Chris Donahue·Zachary C. Lipton·Julian McAuley
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Abstract

Dance Dance Revolution (DDR) is a popular rhythm-based video game. Players perform steps on a dance platform in synchronization with music as directed by on-screen step charts. While many step charts are available in standardized packs, players may grow tired of existing charts, or wish to dance to a song for which no chart exists. We introduce the task of learning to choreograph. Given a raw audio track, the goal is to produce a new step chart. This task decomposes naturally into two subtasks: deciding when to place steps and deciding which steps to select. For the step placement task, we combine recurrent and convolutional neural networks to ingest spectrograms of low-level audio features to predict steps, conditioned on chart difficulty. For step selection, we present a conditional LSTM generative model that substantially outperforms n-gram and fixed-window approaches.

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BibTeXRIS

Chris Donahue, Zachary C. Lipton, Julian McAuley. 2017-06-21. Dance Dance Convolution. https://arxiv.org/abs/1703.06891

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