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arXiv · 2107.12506

TaikoNation: Patterning-focused Chart Generation for Rhythm Action Games

Emily Halina·Matthew Guzdial
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Abstract

Generating rhythm game charts from songs via machine learning has been a problem of increasing interest in recent years. However, all existing systems struggle to replicate human-like patterning: the placement of game objects in relation to each other to form congruent patterns based on events in the song. Patterning is a key identifier of high quality rhythm game content, seen as a necessary component in human rankings. We establish a new approach for chart generation that produces charts with more congruent, human-like patterning than seen in prior work.

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Emily Halina, Matthew Guzdial. 2021-07-26. TaikoNation: Patterning-focused Chart Generation for Rhythm Action Games. https://doi.org/10.1145/3472538.3472589

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