幻觉还是完整性？用于AIGC视频质量评估的几何一致性指标

中文摘要 AI 辅助

近期，AI驱动的视频生成受到广泛关注，这一热潮增加了对可靠视频质量评估（VQA）指标的需求，以评估AI生成内容（AIGC）视频并指导模型优化。现有研究通过视觉协调性、视频-文本一致性和领域特定对齐来评估视频质量，但缺乏衡量对物理定律保真度的量化指标。为解决这一局限，我们提出了一个新颖基准GeoCon-Bench，它基于AIGC视频对物理原理的合规性来评估其质量，通过定量测量生成序列中提取的帧间几何一致性，以此作为估计生成视频符合现实世界物理规则程度的代理指标。具体而言，GeoCon-Bench通过平移估计捕捉全局运动，利用背景对应关系拟合单应性（homography）或基础矩阵（fundamental matrix）模型，并报告包括内点率和几何误差在内的互补指标。我们还发布了包含6类运动的20个场景的数据集。对最先进的AIGC模型开展的实验证明了GeoCon-Bench作为视频质量评估指标的可靠性。

英文摘要

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.

发表机构

• Hunan University（湖南大学）

机构由 AI 辅助整理，请以论文原文为准。