Benchmarking Multi-dimensional AIGC Video Quality Assessment: A Dataset and Unified Model

作者：Zhichao Zhang, Wei Sun, Xinyue Li, Jun Jia, Xiongkuo Min, Zicheng Zhang, Chunyi Li, Zijian Chen, Puyi Wang, Fengyu Sun, Shangling Jui, Guangtao Zhai · 发表于：ACM Transactions on Multimedia Computing Communications and Applications · 年份：2025 · DOI：10.1145/3749844 · 被引用次数：10 · 研究领域：Image and Video Quality Assessment、Advanced Image Processing Techniques、Image Enhancement Techniques

In recent years, AI-driven video generation has gained significant attention due to great advancements in visual and language generative techniques. Consequently, there is a growing need for accurate Video Quality Assessment (VQA) metrics to evaluate the perceptual quality of AI-generated content (AIGC) videos and optimize video generation models. However, assessing the quality of AIGC videos remains a significant challenge because these videos often exhibit highly complex distortions, such as unnatural actions and irrational objects. To address this challenge, we systematically investigate the AIGC-VQA problem in this article, considering both subjective and objective quality assessment perspectives. For the subjective perspective, we construct the L arge-scale G enerated V ideo Q uality Assessment (LGVQ) dataset, consisting of \(2,\!808\) AIGC videos generated by six video generation models using 468 carefully curated text prompts. Unlike previous subjective VQA experiments, we evaluate the perceptual quality of AIGC videos from three critical dimensions: spatial quality, temporal quality, and text-video alignment, which hold utmost importance for current video generation techniques. For the objective perspective, we establish a benchmark for evaluating existing quality assessment metrics on the LGVQ dataset. Our findings show that current metrics perform poorly on this dataset, highlighting a gap in effective evaluation tools. To bridge this gap, we propose the U nify G en...