Retrieval, Refinement, and Ranking for Text-to-Video Generation via Prompt Optimization and Test-Time Scaling

• URL: http://arxiv.org/abs/2603.01509v1
• Date: Mon, 02 Mar 2026 06:35:59 GMT
• Title: Retrieval, Refinement, and Ranking for Text-to-Video Generation via Prompt Optimization and Test-Time Scaling
• Authors: Zillur Rahman, Alex Sheng, Cristian Meo,
• Abstract summary: Large-scale datasets have driven significant progress in Text-to-Video (T2V) generative models.<n>Current methods for improving video output often fall short.<n>We introduce 3R, a novel RAG based prompt optimization framework.
• Score: 1.6671050178877669
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: While large-scale datasets have driven significant progress in Text-to-Video (T2V) generative models, these models remain highly sensitive to input prompts, demonstrating that prompt design is critical to generation quality. Current methods for improving video output often fall short: they either depend on complex, post-editing models, risking the introduction of artifacts, or require expensive fine-tuning of the core generator, which severely limits both scalability and accessibility. In this work, we introduce 3R, a novel RAG based prompt optimization framework. 3R utilizes the power of current state-of-the-art T2V diffusion model and vision language model. It can be used with any T2V model without any kind of model training. The framework leverages three key strategies: RAG-based modifiers extraction for enriched contextual grounding, diffusion-based Preference Optimization for aligning outputs with human preferences, and temporal frame interpolation for producing temporally consistent visual contents. Together, these components enable more accurate, efficient, and contextually aligned text-to-video generation. Experimental results demonstrate the efficacy of 3R in enhancing the static fidelity and dynamic coherence of generated videos, underscoring the importance of optimizing user prompts.

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