AI生成的视频，世界是假的
视频生成器现在能当“可探索的世界”用：你走一步，画面跟着变。但画面好看不等于世界真实——你转个身，刚才那堵墙可能就消失了。这篇论文做了第一套系统评测，让 AI 生成视频后，再“走回去”看同一个地方：墙还在不在、物体大小对不对、换个角度看是不是同一个场景。测了 27 个主流模型，720 个场景，结论是：几乎所有模型都在“3D 一致性”上翻车，画面漂亮，但世界是碎的。它不是你明天能用上的东西，但它告诉你一个真相：现在的 AI 视频，更像一场精心编排的幻觉，而不是一个能走进去的世界。
📄 原文摘要(英文)
Video generators serve as interactive worlds for exploration and simulation, yet realistic and visually appealing frames do not necessarily depict a consistent 3D world. Existing benchmarks assess selected geometric properties or use vision-language models to judge memory and consistency, leaving systematic 3D evaluation underexplored. We introduce Orbia, the first benchmark dedicated to a systematic and comprehensive evaluation of 3D consistency in interactive world models. Through controlled exploration and revisit, we organize the evaluation into three dimensions: input preservation, generated-content persistence, and 3D self-consistency. For input preservation, registered RGB-D references and annotated anchors support appearance, geometry, and spatial stability measurements. For generated-content persistence, we compare revisits to regions absent from the input image with their first generated observations to test whether models preserve what they create. For 3D self-consistency, cross-view depth and feature correspondence, regional scale agreement, and held-out Gaussian rendering test whether generated views describe the same 3D scene. Minute-long exploration and revisits further test long-term consistency. Our construction and annotation pipeline brings together public datasets, self-captured scenes, and simulation, yielding 720 instances across indoor and outdoor environments and six exploration-and-revisit protocols. Evaluation spans 27 text-, action-, and camera-conditioned models through native-interface adapters. Our Orbia benchmark provides a systematic basis for diagnosing 3D inconsistency and developing more persistent video world models.