Parametric Trajectory Distillation for Few-Step Video Generation
Video diffusion and flow models require many sequential evaluations, making generation computationally expensive. Few-step distillation reduces this cost but poses a capacity allocation problem: a student must match the teacher's iterative generation with far less sequential computation. Existing trajectory methods ask the student to reproduce teacher transitions that are highly curved at high noise, which can exceed its capacity and degrade fine detail. We introduce Parametric Trajectory Distillation (PTD), which lets the student parameterize teacher trajectory segments as polynomials and learn from teacher guidance along its own predicted path. PTD is designed to let the learned curvatu...
| Authors | Lan Feng, Peter Karkus, Maximilian Igl et al. |
|---|---|
| Primary category | cs.CV |
| Published | 8 October 2026 |
| Last updated at source | 8 October 2026 |
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