FineDance: A Fine-grained Choreography Dataset for 3D Full Body Dance Generation
This addresses the challenge of fine-grained dance generation for applications in entertainment and animation, though it is incremental as it builds on existing diffusion models and retrieval techniques.
The authors tackled the problem of generating full-body dance sequences from music by introducing FineDance, a dataset with 14.6 hours of music-dance pairs across 22 genres, and FineNet, a method that achieves state-of-the-art performance in reducing monotonous hand motions and improving genre-matching.
Generating full-body and multi-genre dance sequences from given music is a challenging task, due to the limitations of existing datasets and the inherent complexity of the fine-grained hand motion and dance genres. To address these problems, we propose FineDance, which contains 14.6 hours of music-dance paired data, with fine-grained hand motions, fine-grained genres (22 dance genres), and accurate posture. To the best of our knowledge, FineDance is the largest music-dance paired dataset with the most dance genres. Additionally, to address monotonous and unnatural hand movements existing in previous methods, we propose a full-body dance generation network, which utilizes the diverse generation capabilities of the diffusion model to solve monotonous problems, and use expert nets to solve unreal problems. To further enhance the genre-matching and long-term stability of generated dances, we propose a Genre&Coherent aware Retrieval Module. Besides, we propose a novel metric named Genre Matching Score to evaluate the genre-matching degree between dance and music. Quantitative and qualitative experiments demonstrate the quality of FineDance, and the state-of-the-art performance of FineNet. The FineDance Dataset and more qualitative samples can be found at our website.