DanceFields: Learned Deformation Fields for Few-Keypose Dance Style Transfer

Even for the same choreography, different dancers leave distinct impressions—style is not an accessory, but a core component that defines dance. While motion style transfer has predominantly focused on locomotion with consistent stylistic expressions across movements, dance fundamentally breaks this universality: the same style manifests through entirely different body parts and dynamics depending on the choreography. We formalize this choreography-dependent realization as the reading of a style and introduce the task of few-shot dance style transfer, which transfers expressive style while strictly preserving choreographic content. Because this artistic interpretation is extrinsic to visual pose features, it cannot be inferred from content alone and must instead be observed. We propose DanceFields, a representation that disentangles shared choreographic content from choreography-specific style readings. DanceFields learns a shared bank of content-conditioned deformation fields across choreographies, mapping a style to linear coordinates solved analytically in closed form via meta-learning. By observing merely three still keyposes of the target choreography in the desired style, our model resolves the style coordinates without per-style or test-time optimization. Furthermore, DanceFields explicitly models time as style, decoupling local temporal warping from global tempo variations. On a newly captured dataset of 30 choreographies across 16 styles (Laban Effort axes and character traits), DanceFields significantly outperforms per-style-trained baselines in style fidelity, content preservation, and physical plausibility. Crucially, representing style as coordinates naturally unlocks intuitive arithmetic controls—including continuous intensity grading, interpolation, and part-wise compositional editing—and demonstrates robust zero-shot generalization across unseen dance choreographies and even cross-domain locomotion benchmarks.