🤖 AI Summary
This study addresses the challenges novice learners face when engaging with complex online dance tutorials. The authors formally define, for the first time, a five-dimensional framework of dance movement complexity and construct a paired dataset of original and simplified movements. They propose a hybrid rule- and learning-based approach to automatically simplify dance sequences while preserving stylistic integrity. The method integrates a complexity quantification model, a motion simplification algorithm, and a multidimensional evaluation framework. Experimental results demonstrate that 20 professional choreographers rated the simplified movements as natural and stylistically consistent, while 18 novice learners exhibited significant improvements in perceived cognitive load, self-efficacy, and actual performance.
📝 Abstract
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.