AI RESEARCH
Comparison Drives Preference: Reference-Aware Modeling for AI-Generated Video Quality Assessment
arXiv CS.CV
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ArXi:2604.17074v1 Announce Type: new The rapid advancement of generative models has led to a growing volume of AI-generated videos, making the automatic quality assessment of such videos increasingly important. Existing AI-generated content video quality assessment (AIGC-VQA) methods typically estimate visual quality by analyzing each video independently, ignoring potential relationships among videos. In this work, we revisit AIGC-VQA from an inter-video perspective and formulate it as a reference-aware evaluation problem.