[Submitted on 14 Oct 2026]
Title:Orbia: Do Generated Videos Form a Consistent 3D World?
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Abstract: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.
Comments: Technical Report; 70 pages, 43 figures, 12 tables; Project page at this https URL Code at this https URL Dataset at this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2610.20693 [cs.CV]
(or arXiv:2610.20693v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.20693
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Dongyue Lu [view email] [v1] Wed, 14 Oct 2026 17:59:53 UTC (10,040 KB)
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