arXiv AI By Hongchang Liu, Jian Liu, Yujuan Ding, Liang Peng, Xun Jiang, Fei Ma, Xing Xu, Yi Bin

GeoState: Learning View-Decoupled Geometry States for 3D World Modeling

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GeoState introduces a view‑decoupled geometry representation for predictive 3D world modeling, separating scene‑state evolution from camera‑dependent observation. It aggregates multi‑view geometry foundation model features into a scene tokenizer, then uses a geometry decoder to query this state with camera rays. An autoregressive world model predicts future scene states while modeling camera motion independently, enabling consistent decoding under any camera and maintaining a single context‑derived scale throughout rollouts.

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GeoState: Learning View-Decoupled Geometry States for 3D World Modeling

GeoState introduces a view‑decoupled geometry representation for predictive 3D world modeling, separating scene‑state evolution from camera‑dependent observation. It aggregates multi‑view geometry foundation model features into a scene tokenizer, then uses a geometry decoder with camera rays to query the state. An autoregressive world model predicts future scene states while modeling camera motion separately, enabling consistent decoding under any camera and maintaining a single context‑derived scale throughout the rollout.

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