Walk through Paintings : Ego-centric World models from Internet Priors

What if a video generation model could not only imaginea plausible future, but the correct one – accurately reflecting how theworld changes with each action? We answer this by presenting the Ego-centric World Model (EgoWM), a simple, architecture-agnostic methodthat transforms any pre-trained video diffusion model into an action-conditioned world model, enabling precisely controllable future predic-tion. Rather than training from scratch, we repurpose the rich world pri-ors of Internet-scale video models by injecting appropriately compressedmotor commands through lightweight conditioning layers. This allowsour model to follow actions faithfully while preserving generalizationand realism. Our approach scales naturally across embodiments and ac-tion spaces – from 3-DoF mobile robots to 25-DoF humanoids, wherepredicting egocentric joint-angle driven dynamics is substantially morechallenging. The model produces coherent rollouts for both navigationand manipulation, requiring only modest fine-tuning. To evaluate phys-ical correctness independent of appearance, we introduce the StructuralConsistency Score (SCS), which measures whether stable scene elementsevolve consistently with the provided actions. Our method improves SCSby up to 65% over the prior state of the art, Navigation World Models;applies seamlessly to three different video diffusion model architectures;and effectively utilizes Internet priors to generalize to unseen environ-ments, including navigation and manipulation inside paintings. Finally,we demonstrate the applicability of EgoWM to robotic planning.