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 require action supervision for all modalities, limiting scalability to large unlabeled video datasets. In this work, we introduce a two-stage diffusion world model that separates geometric and appearance prediction while maintaining joint temporal coherence. Our key idea is a staggered RGB-depth formulation: depth at time $t+1$ is predicted first using an action-conditioned diffusion transformer, and the RGB frame at the same timestep is then generated conditioned on this predicted depth using a second diffusion model without action inputs. We show that this decoupled design faithfully approximates a hypothetical joint RGBD diffusion model trained with diffusion forcing, enabling the second stage to be trained or fine-tuned on additional RGB-depth data without requiring action labels. Experiments on the CS:GO Dust 2 dataset demonstrate that our approach improves both depth accuracy and RGB fidelity compared to fully joint models and RGB-only baselines, while significantly enhancing scalability to augmented or unlabeled datasets. Our results highlight the effectiveness of geometry-guided diffusion for world modeling and open a path toward large-scale action-free video model training. Code to be released upon acceptance of paper.

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