Academic

VQQA: An Agentic Approach for Video Evaluation and Quality Improvement

arXiv:2603.12310v1 Announce Type: cross Abstract: Despite rapid advancements in video generation models, aligning their outputs with complex user intent remains challenging. Existing test-time optimization methods are typically either computationally expensive or require white-box access to model internals. To address this, we present VQQA (Video Quality Question Answering), a unified, multi-agent framework generalizable across diverse input modalities and video generation tasks. By dynamically generating visual questions and using the resulting Vision-Language Model (VLM) critiques as semantic gradients, VQQA replaces traditional, passive evaluation metrics with human-interpretable, actionable feedback. This enables a highly efficient, closed-loop prompt optimization process via a black-box natural language interface. Extensive experiments demonstrate that VQQA effectively isolates and resolves visual artifacts, substantially improving generation quality in just a few refinement step

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Yiwen Song, Tomas Pfister, Yale Song · · 1 min read · 19 views
#cs.CV #cs.AI #cs.LG #cs.MA

arXiv:2603.12310v1 Announce Type: cross Abstract: Despite rapid advancements in video generation models, aligning their outputs with complex user intent remains challenging. Existing test-time optimization methods are typically either computationally expensive or require white-box access to model internals. To address this, we present VQQA (Video Quality Question Answering), a unified, multi-agent framework generalizable across diverse input modalities and video generation tasks. By dynamically generating visual questions and using the resulting Vision-Language Model (VLM) critiques as semantic gradients, VQQA replaces traditional, passive evaluation metrics with human-interpretable, actionable feedback. This enables a highly efficient, closed-loop prompt optimization process via a black-box natural language interface. Extensive experiments demonstrate that VQQA effectively isolates and resolves visual artifacts, substantially improving generation quality in just a few refinement steps. Applicable to both text-to-video (T2V) and image-to-video (I2V) tasks, our method achieves absolute improvements of +11.57% on T2V-CompBench and +8.43% on VBench2 over vanilla generation, significantly outperforming state-of-the-art stochastic search and prompt optimization techniques.

Executive Summary

This article introduces VQQA, a unified, multi-agent framework for video evaluation and quality improvement. VQQA dynamically generates visual questions and utilizes Vision-Language Model (VLM) critiques as semantic gradients to provide human-interpretable feedback. The method enables a closed-loop prompt optimization process and is applicable to both text-to-video (T2V) and image-to-video (I2V) tasks. Extensive experiments demonstrate that VQQA effectively improves generation quality, significantly outperforming state-of-the-art stochastic search and prompt optimization techniques. The proposed approach has the potential to enhance video generation capabilities and provide actionable feedback for model refinement. However, further research is needed to explore its limitations and scalability.

Key Points

  • ▸ VQQA is a unified, multi-agent framework for video evaluation and quality improvement
  • ▸ VQQA utilizes VLM critiques as semantic gradients for human-interpretable feedback
  • ▸ Applicable to both T2V and I2V tasks, achieving significant improvements over state-of-the-art methods

Merits

Strength

VQQA's ability to provide human-interpretable feedback enables a highly efficient, closed-loop prompt optimization process.

Demerits

Limitation

The scalability of VQQA in large-scale video generation tasks remains unexplored, and its potential limitations in handling diverse user intents need further investigation.

Expert Commentary

The introduction of VQQA marks a significant step towards improving video generation capabilities. By leveraging human-AI collaboration, VQQA provides a novel approach to video evaluation and quality improvement. However, its limitations and scalability need to be explored in further research. The proposed method has the potential to enhance video generation in various applications and raises important questions about the role of AI-generated content in the media industry. As VQQA continues to evolve, it is essential to consider its implications on content moderation policies and the potential for human-AI collaboration in video generation.

Recommendations

  • ✓ Further research is needed to explore the scalability of VQQA in large-scale video generation tasks and its limitations in handling diverse user intents.
  • ✓ Develop content moderation policies that effectively address the growing presence of AI-generated content in the media industry.

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