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arXiv · 2404.12062

MIDGET: Music Conditioned 3D Dance Generation

Jinwu Wang·Wei Mao·Miaomiao Liu
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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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BibTeXRIS

Jinwu Wang, Wei Mao, Miaomiao Liu. 2024-04-18. MIDGET: Music Conditioned 3D Dance Generation. https://doi.org/10.1007/978-981-99-8388-9_23

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