COME: Adding Scene-Centric Forecasting Control to Occupancy World Model

World models are critical for autonomous driving to simulate environmental dynamics and generate synthetic data. Existing methods struggle to disentangle ego-vehicle motion (perspective shifts) from scene evolvement (agent interactions), leading to suboptimal predictions. Instead, we propose to separate environmental changes from ego-motion by leveraging the scene-centric coordinate systems. In this paper, we introduce COME: a framework that integrates scene-centric forecasting Control into the Occupancy world ModEl. Specifically, COME first generates ego-irrelevant, spatially consistent future features through a scene-centric prediction branch, which are then converted into scene condition using a tailored ControlNet. These condition features are subsequently injected into the occupancy world model, enabling more accurate and controllable future occupancy predictions. Experimental results on the nuScenes-Occ3D dataset show that COME achieves consistent and significant improvements over state-of-the-art (SOTA) methods across diverse configurations, including different input sources (ground-truth, camera-based, fusion-based occupancy) and prediction horizons (3s and 8s). For example, under the same settings, COME achieves 26. 3% better mIoU metric than DOME and 23. 7% better mIoU metric than UniScene. These results highlight the efficacy of disentangled representation learning in enhancing spatio-temporal prediction fidelity for world models. Code is available at https: //github. com/synsin0/COME.

Authors

• Yining Shi
• Kun Jiang
• Qiang Meng
• Ke Wang School of Astronautics, Beihang University; Tianmushan Laboratory, Beihang University
• Jiabao Wang Institute of Software, Chinese Academy of Sciences; University of the Chinese Academy of Sciences
• Wenchao Sun
• Tuopu Wen
• MengMeng Yang
• diange yang

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