Make it Simple, Make it Dance: Dance Motion Simplification to Support Novices' Dance Learning
For novices learning dance, this work provides automated motion simplification to make choreography more approachable, addressing a gap in dance education technology.
This study addresses the challenge of dance motion complexity for novices by developing automated simplification methods. Results show that the proposed approaches effectively reduce difficulty while preserving style, validated through evaluations with choreographers and novices.
Online dance tutorials have gained widespread popularity. However, many novices encounter difficulties when dance motion complexity exceeds their skill level, potentially leading to discouragement. This study explores dance motion simplification to address this challenge. We surveyed 30 novices to identify challenging movements, then conducted focus groups with 30 professional choreographers across 10 genres to explore simplification strategies and collect paired original-simplified dance datasets. We identified five complexity factors and developed automated simplification methods using both rule-based and learning-based approaches. We validated our approach through three evaluations. Technical evaluation confirmed our complexity measures and algorithms. 20 professional choreographers assessed motion naturalness, simplification adequacy, and style preservation. 18 novices evaluated learning effectiveness through workload, self-efficacy, objective performance, and perceived difficulty. This work contributes to dance education technology by proposing methods that help make choreography more approachable for beginners while preserving essential characteristics.