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

• URL: http://arxiv.org/abs/2506.13260v1
• Date: Mon, 16 Jun 2025 09:01:09 GMT
• Title: COME: Adding Scene-Centric Forecasting Control to Occupancy World Model
• Authors: Yining Shi, Kun Jiang, Qiang Meng, Ke Wang, Jiabao Wang, Wenchao Sun, Tuopu Wen, Mengmeng Yang, Diange Yang,
• Abstract summary: World models are critical for autonomous driving to simulate environmental dynamics and generate synthetic data.<n>Existing methods struggle to disentangle ego-vehicle motion (perspective shifts from scene evolvement)<n>We propose to separate environmental changes from ego-motion by leveraging the scene-centric coordinate systems.
• Score: 18.815436110557112
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 and videos will be available at https://github.com/synsin0/COME.

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