VQQA: An Agentic Approach for Video Evaluation and Quality Improvement

• URL: http://arxiv.org/abs/2603.12310v1
• Date: Thu, 12 Mar 2026 18:00:00 GMT
• Title: VQQA: An Agentic Approach for Video Evaluation and Quality Improvement
• Abstract summary: We present VQQA (Video Quality Question Answering), a unified framework generalizable across diverse input modalities and video generation tasks.<n>VQQA replaces traditional, passive evaluation metrics with human-interpretable, actionable feedback.<n>Our method achieves absolute improvements of +11.57% on T2V-CompBench and +8.43% on VBench2 over vanilla generation.
• Score: 36.312552904481414
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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