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Music-driven 3D dance generation has become an intensive research topic inrecent years with great potential for real-world applications. Most existingmethods lack the consideration of genre, which results in genre inconsistencyin the generated dance movements. In addition, the correlation between thedance 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 toenhance the genre consistency of long-term dance generation. Second, to improvethe generalization capability of the model, the strategy of pre-training andfine-tuning is adopted.Experimental results on the AIST++ dataset show that theproposed dance generation framework outperforms state-of-the-art methods interms of motion quality and genre consistency.

GTN-Bailando: Genre Consistent Long-Term 3D Dance Generation based on Pre-trained Genre Token Network