# Gen'eLive! Generating Rhythm Actions in Love Live!

arXiv: https://arxiv.org/abs/2202.12823 · HTML: https://mlsanity.com/paper/2202.12823/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 25 Feb 2022.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://mlsanity.com/paper/1901.02860/) (2019)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Procedural Content Generation via Machine Learning (PCGML)](https://mlsanity.com/paper/1702.00539/) (2017)
- [Onsets and Frames: Dual-Objective Piano Transcription](https://mlsanity.com/paper/1710.11153/) (2017)
- [Evolving Mario Levels in the Latent Space of a Deep Convolutional Generative Adversarial Network](https://mlsanity.com/paper/1805.00728/) (2018)
- [Dance Dance Convolution](https://mlsanity.com/paper/1703.06891/) (2017)
- [Explainable PCGML via Game Design Patterns](https://mlsanity.com/paper/1809.09419/) (2018)
- [GenerationMania: Learning to Semantically Choreograph](https://mlsanity.com/paper/1806.11170/) (2018)
- ["Press Space to Fire": Automatic Video Game Tutorial Generation](https://mlsanity.com/paper/1805.11768/) (2018)

## Built on by (most cited first, 4 of 4)

- [Beat-Aligned Spectrogram-to-Sequence Generation of Rhythm-Game Charts](https://mlsanity.com/paper/2311.13687/) (2023)
- [Dance Dance ConvLSTM](https://mlsanity.com/paper/2507.01644/) (2025)
- [ChartGenEval: Corruption-Tested Multi-Dimensional Feedback for Rhythm-Game Chart Generation](https://mlsanity.com/paper/2607.12857/) (2026)
- [ITGPT: A Transformer Based Architecture for the Generation of Dance Dance Revolution and In the Groove Charts](https://mlsanity.com/paper/2607.14148/) (2026)

## Abstract (arXiv)

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, Gen'eLive!, which outperforms the state-of-the-art model by taking into account musical structures through beats and temporal scales. Thanks to its favorable performance, Gen'eLive! 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 Gen'eLive! 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.