MIDGET: Music Conditioned 3D Dance Generation
<|reference_start|>MIDGET: Music Conditioned 3D Dance Generation: In this paper, we introduce a MusIc conditioned 3D Dance GEneraTion model, named MIDGET based on Dance motion Vector Quantised Variational AutoEncoder (VQ-VAE) model and Motion Generative Pre-Training (GPT) model to generate vibrant and highquality dances that match the music rhythm. To tackle challenges in the field, we introduce three new components: 1) a pre-trained memory codebook based on the Motion VQ-VAE model to store different human pose codes, 2) employing Motion GPT model to generate pose codes with music and motion Encoders, 3) a simple framework for music feature extraction. We compare with existing state-of-the-art models and perform ablation experiments on AIST++, the largest publicly available music-dance dataset. Experiments demonstrate that our proposed framework achieves state-of-the-art performance on motion quality and its alignment with the music.<|reference_end|>
@article{wang2024midget:,
    title={MIDGET: Music Conditioned 3D Dance Generation},
    author={Jinwu Wang, Wei Mao, Miaomiao Liu},
    journal={In Australasian Joint Conference on Artificial Intelligence (pp.
  277-288). Singapore: Springer Nature Singapore 2023},
    year={2024},
    doi={10.1007/978-981-99-8388-9_23},
    archivePrefix={arXiv},
    eprint={2404.12062},
    primaryClass={cs.SD cs.CV cs.GR eess.AS}
}