Off-Manifold Refinement: Guiding Video Generators with a Frozen World Model
Modern video generators routinely fail at physical dynamics: objects float, trajectories violate gravity, contacts vanish. Standard denoising and flow-matching objectives fit visual data distributions but do not explicitly penalize such physical violations. Existing remedies can improve physical consistency, but typically add substantial inference or training cost. Candidate-selection methods generate and score multiple videos, while gradient-based world-model guidance repeatedly decodes and re-encodes intermediate estimates. Generator-internal refinement adds perturbation and re-denoising loops, whereas post-training requires curated data and additional optimization. We propose Off-Manif...
| Authors | Hai Nguyen-Truong, Tuan-Anh Vu, Dang Huynh |
|---|---|
| Primary category | cs.CV |
| Published | 30 August 2026 |
| Last updated at source | 30 August 2026 |
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