TaikoNation: Patterning-focused Chart Generation for Rhythm Action Games

TL;DR

This paper introduces TaikoNation, a machine learning approach that generates rhythm game charts with human-like patterning, improving the quality and congruence of game object placement based on song events.

Contribution

The paper presents a novel patterning-focused chart generation method that surpasses previous systems in producing more human-like and congruent rhythm game charts.

Findings

Produces more human-like patterning than prior methods

Enhances the congruence of game object placement

Improves perceived chart quality based on patterning

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.