In the heart of "rhythm games" - games where players must perform actions in
sync with a piece of music - are "charts", the directives to be given to
players. We newly formulate chart generation as a sequence generation task and
train a Transformer using a large dataset. We also introduce tempo-informed
preprocessing and training procedures, some of which are suggested to be
integral for a successful training. Our model is found to outperform the
baselines on a large dataset, and is also found to benefit from pretraining and
finetuning.

通过将图表生成任务作为一个序列生成任务并使用大型数据集训练 Transformer，我们引入了节奏感预处理和训练流程，发现我们的模型在大型数据集上优于基准模型，并且也受益于预训练和微调。

基于节拍对齐的谱图到序列生成的节奏游戏谱面

Beat-Aligned Spectrogram-to-Sequence Generation of Rhythm-Game Charts

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.

该论文提出了一个基于深度生成模型的节奏动作游戏生成方法 Gen'eLive!，其应用可以减少业务操作成本，该模型通过考虑节拍和时间尺度提高了音乐结构的生成效果，并成功地应用于日本 KLab 公司《Love Live!》等游戏的制作中。