FineDance: A Fine-grained Choreography Dataset for 3D Full Body Dance Generation
Abstrak
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 website.
Topik & Kata Kunci
Penulis (7)
Ronghui Li
Junfan Zhao
Yachao Zhang
Mingyang Su
Zeping Ren
Han Zhang
Xiuhua Li
Akses Cepat
PDF tidak tersedia langsung
Cek di sumber asli →- Tahun Terbit
- 2022
- Bahasa
- en
- Total Sitasi
- 102×
- Sumber Database
- Semantic Scholar
- DOI
- 10.1109/ICCV51070.2023.00939
- Akses
- Open Access ✓