Illusion or Integrity? Geometrical Consistency Metric for AIGC Video Quality Evaluation

• URL: http://arxiv.org/abs/2608.09594v1
• Date: Mon, 10 Aug 2026 13:28:07 GMT
• Title: Illusion or Integrity? Geometrical Consistency Metric for AIGC Video Quality Evaluation
• Abstract summary: We present a novel benchmark that evaluates the quality of AIGC videos based on their compliance with physical principles.<n>This serves as a proxy for estimating the extent to which generated videos conform to real-world physical rules.<n>We also release a dataset containing 20 scenes across six motion categories.
• Score: 8.769587010692774
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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