Exploring AIGC Video Quality: A Focus on Visual Harmony, Video-Text Consistency and Domain Distribution Gap
Bowen Qu, Xiaoyu Liang, Shangkun Sun, Wei Gao

TL;DR
This paper proposes a comprehensive framework for assessing AIGC video quality by evaluating visual harmony, video-text consistency, and domain distribution gap, addressing key challenges in AI-generated video evaluation.
Contribution
It introduces specific modules for each assessment dimension and demonstrates improved performance in quality evaluation, winning third place in a related competition.
Findings
Significant variation in quality across different models
Predicting generative source improves assessment accuracy
Proposed method outperforms existing approaches
Abstract
The recent advancements in Text-to-Video Artificial Intelligence Generated Content (AIGC) have been remarkable. Compared with traditional videos, the assessment of AIGC videos encounters various challenges: visual inconsistency that defy common sense, discrepancies between content and the textual prompt, and distribution gap between various generative models, etc. Target at these challenges, in this work, we categorize the assessment of AIGC video quality into three dimensions: visual harmony, video-text consistency, and domain distribution gap. For each dimension, we design specific modules to provide a comprehensive quality assessment of AIGC videos. Furthermore, our research identifies significant variations in visual quality, fluidity, and style among videos generated by different text-to-video models. Predicting the source generative model can make the AIGC video features more…
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Taxonomy
TopicsImage and Video Quality Assessment · Technology and Data Analysis · Innovation in Digital Healthcare Systems
