Dance Dance Convolution

Chris Donahue, Zachary C. Lipton, Julian McAuley

Abstract: Dance Dance Revolution (DDR) is a popular rhythm-based video game. Players perform steps on a dance platform in synchronization with music as directed by on-screen step charts. While many step charts are available in standardized packs, users may grow tired of existing charts, or wish to dance to a song for which no chart exists. We introduce the task of learning to choreograph. Given a raw audio track, the goal is to produce a new step chart. This task decomposes naturally into two subtasks: deciding when to place steps and deciding which steps to select. We demonstrate deep learning solutions for both tasks and establish strong benchmarks for future work.

TL;DR: Introduces the task of automating Dance Dance Revolution choreography; offers a standardized dataset for investigation and a slew of deep learning baselines

Keywords: Deep learning, Supervised Learning, Applications, Games

Conflicts: ucsd.edu, cs.ucsd.edu