VQQA: An Agentic Approach for Video Evaluation and Quality Improvement

TL;DR

VQQA introduces a multi-agent, vision-language framework that improves video generation quality by generating visual questions and using critiques as semantic feedback, enabling efficient, closed-loop prompt optimization.

Contribution

The paper presents VQQA, a novel, generalizable multi-agent system that uses visual question answering and critiques for effective video quality enhancement in a black-box setting.

Findings

Achieves +11.57% improvement on T2V-CompBench

Achieves +8.43% improvement on VBench2

Outperforms existing stochastic search and prompt optimization methods

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.