Learning Style, Reusing Motion: Prior-Guided Unpaired Motion Stylization

Motion stylization controls how a text-specified motion is performed while preserving its content. Paired performances of the same content in different styles are scarce, limiting direct supervision. Pretrained text-to-motion generators offer broad content capabilities and motion knowledge, but style edits may conflict with both. Our framework, TriPrior, coordinates two data priors from unpaired motions with the frozen generator's own motion prior. These priors act at inference time by correcting flow-model velocity predictions or, equivalently, diffusion-model score predictions. The dataset-level prior supplies a stable style direction, the instance-level prior adapts it to the generated motion, and the generative prior provides content-conditioned correction through the generator's response to the edited sampling state. Keeping the data priors outside the generator enables their reuse on HY-Motion, HY-Lite, and Kimodo without backbone retraining. On HumanML3D with style-motion input, TriPrior on HY-Motion achieves 29.40% average accuracy across four independent style classifiers (SRA-4) and 38.70% content-retrieval R@1, exceeding MoMo and MLD+Aberman by 3.06 and 11.32 percentage points, respectively. Controlled ablations show that the generative prior improves content retrieval and motion quality while largely preserving style recognition across the evaluated styles and content motions.