A Diffusion Modeling-Based System for Teaching Dance to Digital Human
Source abstract
Abstract
The introduction of artificial intelligence (AI) has triggered changes in modern dance education. This study investigates the application of diffusion-based modeling and virtual digital humans in dance instruction. Utilizing AI and digital technologies, the proposed system innovatively merges music-driven dance generation with virtual human-based teaching. It achieves this by extracting rhythmic and emotional information from music through audio analysis to generate corresponding dance sequences. The virtual human, functioning as a digital tutor, demonstrates dance movements in real time, enabling students to accurately learn and execute dance postures and rhythms. Analysis of the teaching outcomes, including effectiveness, naturalness, and fluidity, indicates that learning through the digital human results in enhanced user engagement and improved learning outcomes. Additionally, the diversity of dance movements is increased. This system enhances students’ motivation and learning efficacy, offering a novel approach to innovating dance education.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Human Motion and Animation · 3D Shape Modeling and Analysis · Human Pose and Action Recognition
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
The Metaverse and Artificial Intelligence Institute, Wenzhou University, Wenzhou 325000, China
College of Information and Communication Engineering, Dalian Nationalities University, Dalian 116600, China
Funding
Metauniverse and artificial intelligence research and development fund, China
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