Music-driven dance generation can effectively improve the efficiency and popularity of artistic creation, but existing generation methods have problems such as insufficient dance-music correlation, poor stability of long sequence generation, and inconsistent styles.Therefore, a novel dance generation and completion framework that integrates improved transformer and style consistency control is proposed.This framework first constructs a bidirectional attention mechanism cross-modal generation model, enhances the correlation between dance and music through bidirectional interaction perception between music and action modalities, and adopts a planned sampling strategy to alleviate exposure bias in autoregressive generation.By extracting and integrating music features, key action features, and global dance style features, the completed dance segments ensure consistency in music synchronisation and overall style.Experiments showed that the generative model significantly outperformed mainstream comparison models in Frechet distance (25.7), beat coverage (59.7%), hit rate (52.4%), and diversity metrics.The complementary model achieved a style classification accuracy of 95.4% and a style retention rate of 90.2% in dance completion tasks.From this, the model proposed by the research can effectively improve the correlation and style consistency of generated dance, and promote the popularisation of art.
Cao et al. (Thu,) studied this question.