{"ID":2852057,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.18313","arxiv_id":"2510.18313","title":"OmniNWM: Omniscient Driving Navigation World Models","abstract":"Autonomous driving world models are expected to work effectively across three core dimensions: state, action, and reward. Existing models, however, are typically restricted to limited state modalities, short video sequences, imprecise action control, and a lack of reward awareness. In this paper, we introduce OmniNWM, an omniscient panoramic navigation world model that addresses all three dimensions within a unified framework. For state, OmniNWM jointly generates panoramic videos of RGB, semantics, metric depth, and 3D occupancy. A flexible forcing strategy enables high-quality long-horizon auto-regressive generation. For action, we introduce a normalized panoramic Plucker ray-map representation that encodes input trajectories into pixel-level signals, enabling highly precise and generalizable control over panoramic video generation. Regarding reward, we move beyond learning reward functions with external image-based models: instead, we leverage the generated 3D occupancy to directly define rule-based dense rewards for driving compliance and safety. Extensive experiments demonstrate that OmniNWM achieves state-of-the-art performance in video generation, control accuracy, and long-horizon stability, while providing a reliable closed-loop evaluation framework through occupancy-grounded rewards. Project page is available at https://arlo0o.github.io/OmniNWM/.","short_abstract":"Autonomous driving world models are expected to work effectively across three core dimensions: state, action, and reward. Existing models, however, are typically restricted to limited state modalities, short video sequences, imprecise action control, and a lack of reward awareness. In this paper, we introduce OmniNWM,...","url_abs":"https://arxiv.org/abs/2510.18313","url_pdf":"https://arxiv.org/pdf/2510.18313v4","authors":"[\"Bohan Li\",\"Zhuang Ma\",\"Dalong Du\",\"Baorui Peng\",\"Zhujin Liang\",\"Zhenqiang Liu\",\"Chao Ma\",\"Yueming Jin\",\"Hao Zhao\",\"Wenjun Zeng\",\"Xin Jin\"]","published":"2025-10-21T05:49:01Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}