TIDE: Trajectory-Informed Skill Distillation for Efficient Text-to-Video Prompt Optimization
We propose TIDE, a framework that distills this costly search process into an interpretable and reusable prompt-rewriting Skill.
ProofPaper ↗
Key points
- Text-to-video (T2V) models are highly sensitive to prompt specificity, while iterative prompt optimization typically improves generation quality only through repeated video generation and vision-language model evaluation at inference time.
- TIDE formulates prompt rewriting as a set of source-conditioned rule modules, each targeting a distinct semantic dimension.
- Evolved using only 100 prompts over 14 hours, TIDE achieves prompt-optimization performance comparable to computationally expensive search-based methods.
- Moreover, when transferred to previously unseen video-generation models, TIDE outperforms existing approaches specifically designed or trained for those models.
Sources (1)
- [1]TIDE: Trajectory-Informed Skill Distillation for Efficient Text-to-Video Prompt OptimizationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 10, 06:15 PM
We propose TIDE, a framework that distills this costly search process into an interpretable and reusable prompt-rewriting Skill.
Text-to-video (T2V) models are highly sensitive to prompt specificity, while iterative prompt optimization typically improves generation quality only through repeated video generation and vision-language model evaluation at inference time.
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