TaikoNation: Patterning-focused Chart Generation for Rhythm Action Games
arXiv:2107.12506 · doi:10.1145/3472538.3472589
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.
10 pages, 5 figures, Procedural Content Generation Workshop