Decoupling Appearance and Geometry in Diffusion World Models

We study the problem of learning diffusion-based world models that jointly capture scene geometry and visual appearance in complex first-person environments. Existing approaches typically train a single action-conditioned model that predicts future RGB frames, but they either ignore geometry or requ