DanceGen: Supporting Choreography Ideation and Prototyping with Generative AI

TL;DR

DanceGen is an AI-powered tool designed to assist choreographers during the preparation stage by supporting ideation, prototyping, and documentation, thereby enhancing creativity and efficiency in dance creation.

Contribution

This work introduces an interactive AI system for choreographic ideation and prototyping, addressing a gap in digital tools for the initial choreography preparation stage.

Findings

Users reported increased creative efficiency

System expanded creative possibilities

Identified areas for interface and workflow improvements

Abstract

Choreography creation requires high proficiency in artistic and technical skills. Choreographers typically go through four stages to create a dance piece: preparation, studio, performance, and reflection. This process is often individualized, complicated, and challenging due to multiple constraints at each stage. To assist choreographers, most prior work has focused on designing digital tools to support the last three stages of the choreography process, with the preparation stage being the least explored. To address this research gap, we introduce an AI-based approach to assist the preparation stage by supporting ideation, creating choreographic prototypes, and documenting creative attempts and outcomes. We address the limitations of existing AI-based motion generation methods for ideation by allowing generated sequences to be edited and modified in an interactive web interface. This capability is motivated by insights from a formative study we conducted with seven choreographers. We evaluated our system's functionality, benefits, and limitations with six expert choreographers. Results highlight the usability of our system, with users reporting increased efficiency, expanded creative possibilities, and an enhanced iterative process. We also identified areas for improvement, such as the relationship between user intent and AI outcome, intuitive and flexible user interaction design, and integration with existing physical choreography prototyping workflows. By reflecting on the evaluation results, we present three insights that aim to inform the development of future AI systems that can empower choreographers.