Illusion or Integrity? Geometrical Consistency Metric for AIGC Video Quality Evaluation
2026-08-10 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionArtificial Intelligence
AI summaryⓘ
The authors created a new way to check how realistic AI-generated videos are by seeing if they follow real-world physics, like consistent movement between frames. Their method, GeoCon-Bench, looks at how objects move in the video and measures if that movement makes sense mathematically. They introduced a set of tools and data to test this, showing it works well with current AI video models. This helps improve how we judge the quality of AI-made videos beyond just appearance or text matching.
video quality assessmentAI-generated contentgeometric consistencyhomographyfundamental matrixtranslation estimationinlier ratiogeometric errorvideo motionbenchmark dataset
Authors
Yifei Xue, Yuanchen Fei, Hao Zhang, Chenzhi Nie, Tie ji, Yizhen Lao
Abstract
Recently, AI-driven video generation has attracted considerable attention. This surge increases the demand for reliable video quality assessment (VQA) metrics to evaluate AI-generated content (AIGC) videos and guide model optimization. Existing studies assess video quality through visual harmony, video-text consistency, and domain-specific alignment, yet lack quantitative metrics for measuring fidelity to physical laws. To address this limitation, we present a novel benchmark that evaluates the quality of AIGC videos based on their compliance with physical principles by quantitatively measuring geometric consistency across frames extracted from generated sequences. This serves as a proxy for estimating the extent to which generated videos conform to real-world physical rules. Specifically, GeoCon-Bench captures global motion through translation estimation, fits homography or fundamental matrix models using background correspondences, and reports complementary metrics, including inlier ratio and geometric error. We also release a dataset containing 20 scenes across six motion categories. Experiments on state-of-the-art AIGC models demonstrate the reliability of GeoCon-Bench as a video quality assessment metric.