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Generating full-body and multi-genre dance sequences from given music is achallenging task, due to the limitations of existing datasets and the inherentcomplexity of the fine-grained hand motion and dance genres. To address theseproblems, we propose FineDance, which contains 14.6 hours of music-dance paireddata, with fine-grained hand motions, fine-grained genres (22 dance genres),and accurate posture. To the best of our knowledge, FineDance is the largestmusic-dance paired dataset with the most dance genres. Additionally, to addressmonotonous and unnatural hand movements existing in previous methods, wepropose a full-body dance generation network, which utilizes the diversegeneration capabilities of the diffusion model to solve monotonous problems,and use expert nets to solve unreal problems. To further enhance thegenre-matching and long-term stability of generated dances, we propose aGenre&Coherent aware Retrieval Module. Besides, we propose a novel metric namedGenre Matching Score to evaluate the genre-matching degree between dance andmusic. Quantitative and qualitative experiments demonstrate the quality ofFineDance, and the state-of-the-art performance of FineNet. The FineDanceDataset and more qualitative samples can be found at our website.

FineDance: A Fine-grained Choreography Dataset for 3D Full Body Dance Generation