Intelligent Dance Notation: A Dance Movement Quantification Framework Based on Digital Human

Accompanied by the wave of AI, dancing generation has gained significant attention due to its broad applications in dance education, performing arts, and sports science. Dance movement quantification, aiming to accurately quantify dance movements for dance modeling, is a key technique on dancing notation. Due to the lack of a unified standard and the subjective nature of traditional dance notations, these notations have difficulty in accurately recording dance movements. This paper introduces an innovative framework called “Intelligent Dance Notation”, designed to quantify dance movements using digital humans. By leveraging Artificial Intelligence (AI) achievement, this framework quantifies both artistic and real-life dance movements, recording them visually in 3D space through digital humans. Specifically, for artistic dance movements, an AI system is developed to semi-automatically extract quantitative data from dance images. For real-life dance movements, the AI system captures dancers’ limb and finger movements in real time using motion capture tools or directly analyzes dance videos to extract quantitative data. Finally, a digital human is created to present and record the extracted quantitative data, forming the “Intelligent Dance Notation”. This novel approach to accurately quantifying and recording dance movements holds significant potential for enhancing dance teaching, creation, cultural heritage preservation, and the development of dance art.