Beyond Pixel Histories: World Models with Persistent 3D State

Abstract:Interactive world models continually generate video by responding to a user's actions, enabling open-ended generation capabilities. However, existing models typically lack a 3D representation of the environment, meaning 3D consistency must be implicitly learned from data, and spatial memory is restricted to limited temporal context windows. This results in an unrealistic user experience and presents significant obstacles to downstream tasks such as training agents. To address this, we present PERSIST, a new paradigm of world model which simulates the evolution of a latent 3D scene: environment, camera, and renderer. This allows us to synthesise new frames with persistent spatial memory and consistent geometry. Both quantitative metrics and a qualitative user study show substantial improvements in spatial memory, 3D consistency, and long-horizon stability over existing methods, enabling coherent, evolving 3D worlds. We further demonstrate novel capabilities, including synthesising diverse 3D environments from a single image, as well as enabling fine-grained, geometry-aware control over generated experiences by supporting environment editing and specification directly in 3D space. Project page: this https URL

Accepted to the International Conference on Machine Learning (ICML) 2026. To appear in the Proceedings of Machine Learning Research (PMLR). 9 pages

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2603.03482 [cs.CV]

(or arXiv:2603.03482v2 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2603.03482 Focus to learn more arXiv-issued DOI via DataCite

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