  Music-driven 3D dance generation has become an intensive research topic in
recent years with great potential for real-world applications. Most existing
methods lack the consideration of genre, which results in genre inconsistency
in the generated dance movements. In addition, the correlation between the
dance genre and the music has not been investigated. To address these issues,
we propose a genre-consistent dance generation framework, GTN-Bailando. First,
we propose the Genre Token Network (GTN), which infers the genre from music to
enhance the genre consistency of long-term dance generation. Second, to improve
the generalization capability of the model, the strategy of pre-training and
fine-tuning is adopted.Experimental results on the AIST++ dataset show that the
proposed dance generation framework outperforms state-of-the-art methods in
terms of motion quality and genre consistency.
