We address the problem of generating a 3D-consistent, navigable environment that is spatially grounded: a simulation of a real location. Existing video generative models can produce a plausible sequence that is consistent with a text (T2V) or image (I2V) prompt. However, the capability to reconstruct the real world under arbitrary weather conditions and dynamic object configurations is essential for downstream applications including autonomous driving and robotics simulation. To this end, we present CityRAG, a video generative model that leverages large corpora of geo-registered data as context to ground generation to the physical scene, while maintaining learned priors for complex motion and appearance changes. CityRAG relies on temporally unaligned training data, which teaches the model to semantically disentangle the underlying scene from its transient attributes. Our experiments demonstrate that CityRAG can generate coherent minutes-long, physically grounded video sequences, maintain weather and lighting conditions over thousands of frames, achieve loop closure, and navigate complex trajectories to reconstruct real-world geography.

翻译：我们解决了生成具有三维一致性和可导航环境的问题，该环境在空间上锚定于真实地点的模拟。现有视频生成模型能产生与文本（T2V）或图像（I2V）提示一致的合理序列。然而，在任意天气条件和动态物体配置下重建真实世界的能力，对于自动驾驶和机器人仿真等下游应用至关重要。为此，我们提出CityRAG，一种视频生成模型，它利用海量地理注册数据作为上下文，将生成过程锚定到物理场景，同时保持复杂运动与外观变化的学习先验。CityRAG依赖时间上未对齐的训练数据，这教会模型从瞬态属性中语义解耦底层场景。我们的实验表明，CityRAG能生成连贯数分钟、物理锚定的视频序列，在数千帧内维持天气与光照条件，实现闭环检测，并导航复杂轨迹以重建真实世界地理。