Walk Through Paintings : Ego-centric World Models from Internet Priors

We present Egocentric World Model (EgoWM), a simple, architecture-agnostic method that transforms any pre-trained video diffusion model into an action-conditioned world model, enabling controllable prediction of the future. Rather than training from scratch, we re-purpose the rich world priors of Internet-scale video models, injecting motor commands through lightweight conditioning layers. This allows our model to follow actions faithfully, while preserving generalization and realism. Our approach scales naturally across embodiments and action spaces — from 3-DoF mobile robots to 25-DoF humanoids, where predicting egocentric joint-angle–driven dynamics is substantially more challenging. The model produces coherent rollouts for both navigation and manipulation, requiring only modest fine-tuning. To evaluate physical correctness independent of appearance, we introduce the Structural Consistency Score (SCS), which measures whether stable scene elements evolve consistently with the provided actions. Our method improves SCS by up to 80% over prior state-of-the-art (Navigation World Models) while exhibiting up to 6x lower latency and generalizing robustly to unseen environments — including navigation inside paintings.