🤖 AI Summary
This work addresses the labor-intensive process of chart authoring in rhythm games such as Dance Dance Revolution and In the Groove by proposing ITGPT, the first end-to-end Transformer-based model for automatic chart generation. ITGPT jointly models audio and rhythmic features to achieve precise alignment between music and dance step sequences. The proposed method significantly outperforms existing approaches in both generation quality and computational efficiency: it produces charts that better adhere to rhythmic patterns while substantially reducing inference overhead. This advancement establishes a highly effective and practical paradigm for automated content creation in rhythm games.
📝 Abstract
Dance Dance Revolution and In the Groove are rhythm games consisting of songs and accompanying choreography, referred to as charts. Players press arrows on a device referred to as a dance pad in time with steps determined by the song's chart. The process of manual chart generation is timestaking and difficult, motivating interest in automation. We propose ITGPT, a new transformer based architecture for the generation of DDR/ITG charts, and demonstrate significant improvements to generation accuracy and computational cost in comparison to predecessor work.