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Walking the Score Manifold: Continuous-time Generative Dynamics on Learned Data Manifolds
Generative modeling of time-dependent data is typically formulated on a discrete temporal grid, restricting supervision to the observed timestamps in the training data. We instead frame generation as continuous-time evolution on a learned data manifold. To this end, we leverage pretrained score-based models as geometric priors and learn a vector field that evolves data along score-induced interpolation paths. Because these dynamics follow transitions that respect the geometry learned by the score model, they support generation at arbitrary timestamps and temporal super-resolution beyond the di
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-15T22:42:24.000Z
First collected: 2026-09-20T08:20:57.646Z. This is not the publication date.