Prompt-A-Video: Prompt Your Video Diffusion Model via Preference-Aligned LLM
Prompt-A-Video leverages preference-aligned LLMs with reward-guided evolution, boosting video quality metrics by over 0.2 on average across models.
Key Findings
Methodology
This work introduces a two-stage automatic prompt optimization framework combining reward-guided evolution and preference alignment. First, a multi-dimensional reward system evaluates generated videos, constructing a pool of high-quality prompts. Then, leveraging evolutionary algorithms and GPT-4o, the system automatically refines prompts to align with model preferences. Fine-tuning the large language model via supervised fine-tuning (SFT) and direct preference optimization (DPO) further enhances its ability to generate video-centric prompts. Extensive experiments on WebVid and VBench datasets demonstrate significant improvements in multiple quality metrics, with average gains exceeding 0.2 points over baseline prompts. The framework effectively addresses challenges such as modality inconsistency, high annotation costs, and model unawareness, providing a scalable solution for diverse text-to-video models.
Key Results
- On WebVid, prompts refined through DPO-2 improved the average visual quality (VQ) score from 3.079 to 3.254 for Open-Sora 1.2, and similar improvements were observed in temporal consistency and dynamic degree metrics. The overall video quality metrics showed an average increase of over 0.2 points, indicating clearer, more coherent, and more engaging videos.
- In VBench, the proposed method enhanced subject and background consistency, motion smoothness, and aesthetic quality, with average improvements exceeding 0.15. Human subjective evaluations revealed over 80% win rates for prompts optimized with Prompt-A-Video, confirming its superiority in perceptual quality.
- The cross-model generalization experiments demonstrated that prompts optimized for one model could effectively transfer to others, indicating robustness. The multi-round DPO training further refined the prompts, leading to consistent performance gains across different architectures and datasets.
Significance
This research marks a significant advancement in automated prompt engineering for video synthesis. By integrating reward-based evaluation and preference alignment within a large language model framework, it reduces reliance on manual prompt crafting and domain expertise. The approach addresses longstanding issues such as modality mismatch and high annotation costs, paving the way for more accessible, scalable, and high-quality video content creation. Its implications extend to industries like entertainment, virtual reality, and gaming, where rapid, high-fidelity video generation is crucial. Furthermore, the methodology offers a blueprint for future multi-modal content optimization, fostering innovation in AI-driven creative workflows.
Technical Contribution
The core technical innovation lies in the combination of multi-dimensional reward evaluation, evolutionary prompt refinement, and large-scale language model fine-tuning. The reward system assesses video quality across visual, temporal, and semantic dimensions, guiding the evolution of prompts. The use of GPT-4o as an evolutionary operator enables automatic, model-specific prompt enhancement. The two-stage training pipeline—supervised fine-tuning with curated prompt pairs and DPO for preference alignment—ensures that the language model learns to generate prompts that are both effective and aligned with model preferences. This integrated framework surpasses existing static or manually crafted prompt methods, offering a dynamic, scalable solution for multi-model video synthesis.
Novelty
This work is the first to introduce an automated, preference-aware prompt optimization system specifically tailored for video generation tasks. Unlike prior image-focused prompt refinement methods, it explicitly considers the dynamic and temporal aspects of videos. The integration of reward-guided evolution with large language models for prompt enhancement is a novel approach, addressing the core challenges of modality mismatch, high annotation costs, and model unawareness. The framework's ability to generate model-preferred prompts without manual intervention represents a significant leap forward in AI content creation.
Limitations
- The computational cost of multi-round evolution and reward evaluation remains high, especially for large-scale video datasets, limiting real-time applications.
- The framework's effectiveness depends on the quality and diversity of the reward models; in scenarios with limited or biased rewards, performance may degrade.
- While the method generalizes across different models, extreme or novel content types may still pose challenges, requiring further adaptation or retraining.
Future Work
Future research will focus on improving the efficiency of the prompt evolution process, possibly through reinforcement learning or meta-learning techniques. Expanding the reward system to incorporate more nuanced dynamic and semantic metrics could further enhance video quality. Additionally, integrating multimodal cues such as audio and text descriptions will enable richer content generation. Exploring real-time prompt adaptation and extending the framework to longer, more complex videos are promising directions. Ultimately, the goal is to develop a fully autonomous, scalable system capable of producing diverse, high-fidelity videos tailored to user preferences and application needs.
AI Executive Summary
The rapid development of AI-driven video synthesis has revolutionized digital content creation, yet challenges remain in producing high-quality, user-aligned videos efficiently. Traditional methods rely heavily on manual prompt engineering, which is labor-intensive and often yields inconsistent results. Existing automatic approaches tend to struggle with modality mismatch, high annotation costs, and model unawareness, especially in the complex domain of text-to-video generation. These limitations hinder the widespread adoption of AI-generated videos in entertainment, education, and virtual reality applications.
In response, this study introduces Prompt-A-Video, a novel framework that leverages large language models (LLMs) guided by reward-based evaluation and preference alignment to automatically generate optimized prompts for video diffusion models. The core innovation is a two-stage process: first, constructing a multi-dimensional reward system that assesses video quality across visual fidelity, temporal coherence, and semantic alignment; second, employing a reward-guided evolutionary algorithm combined with GPT-4o to iteratively refine prompts. This process produces a high-quality prompt pool tailored to specific models, which is then used to fine-tune the LLM via supervised learning and direct preference optimization (DPO). The result is a model capable of autonomously generating prompts that are both effective and aligned with the preferences of different video diffusion architectures.
Extensive experiments on WebVid and VBench datasets demonstrate the effectiveness of Prompt-A-Video. Quantitative metrics such as VQ, TC, and DD show average improvements exceeding 0.2 points after DPO-2 optimization, indicating clearer, more coherent, and more dynamic videos. Human evaluations further confirm that videos generated with prompts refined by Prompt-A-Video outperform those from baseline prompts and other state-of-the-art methods, with win rates surpassing 80%. These results highlight the framework’s robustness, generalizability, and potential to significantly advance automated video content creation.
The broader impact of this work lies in democratizing high-quality video synthesis, reducing reliance on expert knowledge, and enabling scalable content production. It opens avenues for AI-assisted filmmaking, personalized virtual environments, and real-time content adaptation. Nonetheless, challenges such as computational costs and the need for more nuanced reward systems remain. Future efforts will aim to optimize efficiency, incorporate multimodal feedback, and extend the approach to longer, more complex videos. Overall, Prompt-A-Video represents a pivotal step toward autonomous, preference-aware AI content generation, promising to reshape the landscape of digital media production.
Deep Dive
Abstract
Text-to-video models have made remarkable advancements through optimization on high-quality text-video pairs, where the textual prompts play a pivotal role in determining quality of output videos. However, achieving the desired output often entails multiple revisions and iterative inference to refine user-provided prompts. Current automatic methods for refining prompts encounter challenges such as Modality-Inconsistency, Cost-Discrepancy, and Model-Unaware when applied to text-to-video diffusion models. To address these problem, we introduce an LLM-based prompt adaptation framework, termed as Prompt-A-Video, which excels in crafting Video-Centric, Labor-Free and Preference-Aligned prompts tailored to specific video diffusion model. Our approach involves a meticulously crafted two-stage optimization and alignment system. Initially, we conduct a reward-guided prompt evolution pipeline to automatically create optimal prompts pool and leverage them for supervised fine-tuning (SFT) of the LLM. Then multi-dimensional rewards are employed to generate pairwise data for the SFT model, followed by the direct preference optimization (DPO) algorithm to further facilitate preference alignment. Through extensive experimentation and comparative analyses, we validate the effectiveness of Prompt-A-Video across diverse generation models, highlighting its potential to push the boundaries of video generation.
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