用几何一致性评估AI生成视频是否符合物理规律
Illusion or Integrity? Geometrical Consistency Metric for AIGC Video Quality Evaluation
- 通过帧间几何关系量化视频物理合理性
- 在20个场景6类运动上验证了评估可靠性
- 适合关注真实感的AIGC视频研究者
近期，基于AI的视频生成受到广泛关注。这一趋势增加了对可靠视频质量评估（VQA）指标的需求，以衡量AI生成内容（AIGC）视频并指导模型优化。现有研究主要关注视觉和谐性、视频-文本一致性及领域特定对齐，但缺乏对物理规律遵循程度的定量度量。为解决此问题，我们提出一种新基准GeoCon-Bench，通过量化生成视频序列中提取帧之间的几何一致性，评估其对物理原理的遵守程度。该指标作为真实世界物理规则符合度的代理。具体而言，GeoCon-Bench通过平移估计捕捉全局运动，利用背景对应点拟合单应性或基础矩阵模型，并报告内点率和几何误差等互补指标。我们还发布了包含20个场景、覆盖六类运动的数据集。在多个先进AIGC模型上的实验表明，GeoCon-Bench具备可靠的视频质量评估能力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。