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Driving 3D characters to dance following a piece of music is highlychallenging due to the spatial constraints applied to poses by choreographynorms. In addition, the generated dance sequence also needs to maintaintemporal coherency with different music genres. To tackle these challenges, wepropose a novel music-to-dance framework, Bailando, with two powerfulcomponents: 1) a choreographic memory that learns to summarize meaningfuldancing units from 3D pose sequence to a quantized codebook, 2) an actor-criticGenerative Pre-trained Transformer (GPT) that composes these units to a fluentdance coherent to the music. With the learned choreographic memory, dancegeneration is realized on the quantized units that meet high choreographystandards, such that the generated dancing sequences are confined within thespatial constraints. To achieve synchronized alignment between diverse motiontempos and music beats, we introduce an actor-critic-based reinforcementlearning scheme to the GPT with a newly-designed beat-align reward function.Extensive experiments on the standard benchmark demonstrate that our proposedframework achieves state-of-the-art performance both qualitatively andquantitatively. Notably, the learned choreographic memory is shown to discoverhuman-interpretable dancing-style poses in an unsupervised manner.

Bailando: 3D Dance Generation by Actor-Critic GPT with Choreographic Memory