VQQA: An Agentic Approach for Video Evaluation and Quality Improvement
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| Format: | Preprint |
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2026
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| _version_ | 1866918384624467968 |
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| author | Song, Yiwen Pfister, Tomas Song, Yale |
| author_facet | Song, Yiwen Pfister, Tomas Song, Yale |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_12310 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | VQQA: An Agentic Approach for Video Evaluation and Quality Improvement Song, Yiwen Pfister, Tomas Song, Yale Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Multiagent Systems 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. |
| title | VQQA: An Agentic Approach for Video Evaluation and Quality Improvement |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2603.12310 |