Generative video models are increasingly studied as implicit world models, yet evaluating whether they produce physically plausible 3D structure and motion remains challenging. Most existing video evaluation pipelines rely heavily on human judgment or learned graders, which can be subjective and weakly diagnostic for geometric failures. We introduce PDI-Bench (Perspective Distortion Index), a quantitative framework for auditing geometric coherence in generated videos. Given a generated clip, we obtain object-centric observations via segmentation and point tracking (e.g., SAM 2, MegaSaM, and CoTracker3), lift them to 3D world-space coordinates via monocular reconstruction, and compute a set of projective-geometry residuals capturing three failure dimensions: scale-depth alignment, 3D motion consistency, and 3D structural rigidity. To support systematic evaluation, we build PDI-Dataset, covering diverse scenarios designed to stress these geometric constraints. Across state-of-the-art video generators, PDI reveals consistent geometry-specific failure modes that are not captured by common perceptual metrics, and provides a diagnostic signal for progress toward physically grounded video generation and physical world model. Our code and dataset can be found at https://pdi-bench.github.io/.

翻译：生成式视频模型作为隐式世界模型的研究日益增多，但评估其是否产生物理上合理的3D结构与运动仍具挑战。现有视频评估管道大多依赖人工判断或学习型评分器，此类方法具有主观性且难以诊断几何故障。我们提出PDI-Bench（透视畸变指数），一种用于检测生成视频几何一致性的量化框架。给定生成片段，通过分割与点追踪（如SAM 2、MegaSaM及CoTracker3）获取以物体为中心的观测，经单目重建将其提升至3D世界空间坐标，并计算一组投影几何残差以捕获三类故障维度：尺度-深度对齐、3D运动一致性及3D结构刚性。为支持系统性评估，我们构建了涵盖旨在检验这些几何约束的多样化场景的PDI-Dataset。在现有最优视频生成器中，PDI揭示了常见感知指标未能捕获的、与几何相关的特定故障模式，并为迈向基于物理的视频生成及物理世界模型提供了诊断信号。我们的代码与数据集见https://pdi-bench.github.io/。