# Dance Dance ConvLSTM

arXiv: https://arxiv.org/abs/2507.01644 · HTML: https://mlsanity.com/paper/2507.01644/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 2 Jul 2025.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [TaikoNation: Patterning-focused Chart Generation for Rhythm Action Games](https://mlsanity.com/paper/2107.12506/) (2021)
- [GenerationMania: Learning to Semantically Choreograph](https://mlsanity.com/paper/1806.11170/) (2018)
- [Gen'eLive! Generating Rhythm Actions in Love Live!](https://mlsanity.com/paper/2202.12823/) (2022)

## Abstract (arXiv)

textitDance Dance Revolution is a rhythm game 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. In 2017, the authors of Dance Dance Convolution (DDC) developed an algorithm for the automatic generation of textitDance Dance Revolution charts, utilizing a CNN-LSTM architecture. We introduce Dance Dance ConvLSTM (DDCL), a new method for the automatic generation of DDR charts using a ConvLSTM based model, which improves upon the DDC methodology and substantially increases the accuracy of chart generation.