MIDGET: Music Conditioned 3D Dance Generation

• URL: http://arxiv.org/abs/2404.12062v1
• Date: Thu, 18 Apr 2024 10:20:37 GMT
• Title: MIDGET: Music Conditioned 3D Dance Generation
• Authors: Jinwu Wang, Wei Mao, Miaomiao Liu,
• Abstract summary: We introduce a MusIc conditioned 3D Dance GEneraTion model, named MIDGET, 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 ablations, and 3) a simple framework for music feature extraction.
• Score: 13.067687949642641
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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