NeuIDO: Neural Intrinsic Dynamics Operator for Physics-Informed 4D World Models

World models aim to capture environmental dynamics andpredict future trajectories, showing growing potential for embodied in-telligence. Physics-informed 4D generation integrates physical simulationto predict 3D object interactions, offering a promising pathway towardworld models. However, this paradigm relies on manually imposed dy-namical assumptions rather than internalizing world dynamics, and thusstill leaves a gap toward a true world model. To bridge this gap, we pro-pose NeuIDO, a novel world dynamics modeling framework that learnsa unified intrinsic dynamics representation from visual observations, ad-vancing physics-informed 4D generation toward a world model. Specifi-cally, we formulate world modeling as a neural operator learning problemand introduce a two-stage training strategy to learn a generalizable map-ping from the visual observation distribution to the intrinsic dynamicsdistribution. Building on this observation-dynamics mapping, NeuIDOenables zero-shot dynamics inference directly from videos and can be fur-ther aligned with complex real-world dynamics via few-shot adaptation.Extensive experiments demonstrate that NeuIDO effectively unifies theintrinsic dynamics underlying diverse visual observations into a sharedrepresentation and rapidly infers dynamics in novel scenes.