# Multilevel Semantic-Aware Model for AI-Generated Video Quality Assessment Source: [Multilevel Semantic-Aware Model for AI-Generated Video Quality Assessment]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:55:27.100Z Authors: Jiaze Li, Haoran Xu, Shiding Zhu, Junwei He, Haozhao Wang Published: 2025-01-06T01:18:11.000Z ## Abstract The rapid development of diffusion models has greatly advanced AI-generated videos in terms of length and consistency recently, yet assessing AI-generated videos still remains challenging. Previous approaches have often focused on User-Generated Content(UGC), but few have targeted AI-Generated Video Quality Assessment methods. In this work, we introduce MSA-VQA, a Multilevel Semantic-Aware Model for AI-Generated Video Quality Assessment, which leverages CLIP-based semantic supervision and cross-attention mechanisms. Our hierarchical framework analyzes video content at three levels: frame, segment, and video. We propose a Prompt Semantic Supervision Module using text encoder of CLIP to ensure semantic consistency between videos and conditional prompts. Additionally, we propose the Semantic Mutation-aware Module to capture subtle variations between frames. Extensive experiments demonstrate our method achieves state-of-the-art results. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [AIGC Video Quality Assessment on T2VQA-DB (test)](): AIGC Video Quality Assessment; T2VQA-DB (test); SRCC: 0.81. ## Original sources and implementation [Original paper]() [PDF]()