Dance Dance ConvLSTM

TL;DR

This paper introduces Dance Dance ConvLSTM, a ConvLSTM-based model that significantly improves the accuracy of automatically generating Dance Dance Revolution charts compared to previous CNN-LSTM methods.

Contribution

The paper presents a novel ConvLSTM model for DDR chart generation, enhancing accuracy over the existing CNN-LSTM approach.

Findings

Improved chart generation accuracy with DDCL.

Outperforms previous CNN-LSTM based methods.

Demonstrates effectiveness on DDR chart datasets.

Abstract

\textit{Dance 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 \textit{Dance 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.