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Generating 3D dances from music is an emerged research task that benefits alot of applications in vision and graphics. Previous works treat this task assequence generation, however, it is challenging to render a music-alignedlong-term sequence with high kinematic complexity and coherent movements. Inthis paper, we reformulate it by a two-stage process, ie, a key pose generationand then an in-between parametric motion curve prediction, where the key posesare easier to be synchronized with the music beats and the parametric curvescan be efficiently regressed to render fluent rhythm-aligned movements. Wenamed the proposed method as DanceFormer, which includes two cascadingkinematics-enhanced transformer-guided networks (called DanTrans) that tackleeach stage, respectively. Furthermore, we propose a large-scale musicconditioned 3D dance dataset, called PhantomDance, that is accurately labeledby experienced animators rather than reconstruction or motion capture. Thisdataset also encodes dances as key poses and parametric motion curves apartfrom pose sequences, thus benefiting the training of our DanceFormer. Extensiveexperiments demonstrate that the proposed method, even trained by existingdatasets, can generate fluent, performative, and music-matched 3D dances thatsurpass previous works quantitatively and qualitatively. Moreover, the proposedDanceFormer, together with the PhantomDance dataset(https://github.com/libuyu/PhantomDanceDataset), are seamlessly compatible withindustrial animation software, thus facilitating the adaptation for variousdownstream applications.

DanceFormer: Music Conditioned 3D Dance Generation with Parametric Motion Transformer