InfiniteDance: Scalable 3D Dance Generation Towards in-the-wild Generalization

Although existing 3D dance generation methods performwell in controlled scenarios, they often struggle to generalize in the wild.When conditioned on unseen music, existing methods often produce un-structured or physically implausible dance, largely due to limited music-to-dance data and restricted model capacity. This work aims to pushthe frontier of generalizable 3D dance generation by scaling up bothdata and model design. 1) On the data side, we develop a fully auto-mated pipeline that reconstructs high-fidelity 3D dance motions frommonocular videos. To eliminate the physical artifacts prevalent in exist-ing reconstruction methods, we introduce a Foot Restoration DiffusionModel (FRDM) guided by foot-contact and geometric constraints thatenforce physical plausibility while preserving kinematic smoothness andexpressiveness, resulting in a diverse, high-quality multimodal 3D dancedataset totaling 100.69 hours. 2) On model design, we propose Choreo-graphic LLaMA (ChoreoLLaMA), a scalable LLaMA-based architecture.To enhance robustness under unfamiliar music conditions, we integratea retrieval-augmented generation (RAG) module that injects referencedance as a prompt. Additionally, we design a slow/fast-cadence Mixture-of-Experts (MoE) module that enables ChoreoLLaMA to smoothly adaptmotion rhythms across varying music tempos. Extensive experimentsacross diverse dance genres show that our approach surpasses existingmethods in both qualitative and quantitative evaluations, marking a steptoward scalable, real-world 3D dance generation.