VQ-Insight: Teaching VLMs for AI-Generated Video Quality Understanding via Progressive Visual Reinforcement Learning

• URL: http://arxiv.org/abs/2506.18564v1
• Date: Mon, 23 Jun 2025 12:20:14 GMT
• Title: VQ-Insight: Teaching VLMs for AI-Generated Video Quality Understanding via Progressive Visual Reinforcement Learning
• Authors: Xuanyu Zhang, Weiqi Li, Shijie Zhao, Junlin Li, Li Zhang, Jian Zhang,
• Abstract summary: VQ-Insight is a novel reasoning-style framework for AIGC video quality assessment.<n>It combines image quality warm-up, general task-specific temporal learning, and joint optimization with the video generation model.<n>It consistently outperforms state-of-the-art baselines in preference comparison, multi-dimension scoring, and natural video scoring.
• Score: 21.35520258725298
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in AI-generated content (AIGC) have led to the emergence of powerful text-to-video generation models. Despite these successes, evaluating the quality of AIGC-generated videos remains challenging due to limited generalization, lack of temporal awareness, heavy reliance on large-scale annotated datasets, and the lack of effective interaction with generation models. Most current approaches rely on supervised finetuning of vision-language models (VLMs), which often require large-scale annotated datasets and tend to decouple understanding and generation. To address these shortcomings, we propose VQ-Insight, a novel reasoning-style VLM framework for AIGC video quality assessment. Our approach features: (1) a progressive video quality learning scheme that combines image quality warm-up, general task-specific temporal learning, and joint optimization with the video generation model; (2) the design of multi-dimension scoring rewards, preference comparison rewards, and temporal modeling rewards to enhance both generalization and specialization in video quality evaluation. Extensive experiments demonstrate that VQ-Insight consistently outperforms state-of-the-art baselines in preference comparison, multi-dimension scoring, and natural video scoring, bringing significant improvements for video generation tasks.

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