Music-driven generative dance movement teaching game based on a multi-feature fusion strategy

Abstract

The study aims to develop a music-driven dance teaching game that enhances players' dance learning. By utilizing a multi-feature fusion strategy, the game creates dance movements inspired by music. The model effectively combines music and dance features to generate various dance sequences. In performance tests, the research model successfully matches the tempo and intensity of the music with the generated dance. Overall, this approach improves players' dance movement learning ability. The teaching effectiveness of the research method is significantly superior, with an average value of 95.23 points in the teaching evaluation. In terms of running time, for simple dances, the average generation time is 10 s; for ordinary categories of dances, the average generation time is 15 s; for complex dances, the average generation time is 17 s. The fluency of the generated dances is 97.21 %. The study's findings confirm the effectiveness of the research method. The game's use of multi-feature fusion algorithms allows for adaptive adjustments in difficulty and complexity based on the player's learning situation and ability level. This provides a more diverse and immersive gaming experience, while also presenting innovative ideas for future game development and teaching design.