GenéLive! Generating Rhythm Actions in Love Live!
This work addresses the challenge of automating chart generation for rhythm action games, such as Love Live! with over 10 million users, reducing operational costs for game developers, though it appears incremental as it builds on existing generative methods.
The paper tackles the problem of generating rhythm action game charts by introducing GenéLive!, a deep generative model that incorporates musical structures like beats and temporal scales, which outperforms the state-of-the-art model and reduced business costs by up to half at KLab Inc.
This article presents our generative model for rhythm action games together with applications in business operations. Rhythm action games are video games in which the player is challenged to issue commands at the right timings during a music session. The timings are rendered in the chart, which consists of visual symbols, called notes, flying through the screen. We introduce our deep generative model, GenéLive!, which outperforms the state-of-the-art model by taking into account musical structures through beats and temporal scales. Thanks to its favorable performance, GenéLive! was put into operation at KLab Inc., a Japan-based video game developer, and reduced the business cost of chart generation by as much as half. The application target included the phenomenal "Love Live!," which has more than 10 million users across Asia and beyond, and is one of the few rhythm action franchises that has led the online era of the genre. In this article, we evaluate the generative performance of GenéLive! using production datasets at KLab as well as open datasets for reproducibility, while the model continues to operate in their business. Our code and the model, tuned and trained using a supercomputer, are publicly available.