StyledStreets：具有空间和时间一致性的多风格街道模拟器
StyledStreets: Multi-style Street Simulator with Spatial and Temporal Consistency
AI总结：
StyledStreets提出了一种多风格街道模拟器，通过混合嵌入、不确定性感知渲染和统一参数模型，在保持空间和时间一致性的同时实现指令驱动的照片级风格迁移，应用于自动驾驶测试和增强现实。
摘要：
城市场景重建需要同时建模静态基础设施和动态元素，并支持多样化的环境条件。我们提出了StyledStreets，一种多风格街道模拟器，能够实现指令驱动的场景编辑，并保证空间和时间的一致性。基于最先进的用于街道场景的高斯泼溅框架，并通过我们提出的姿态优化和多视角训练进行增强，我们的方法通过三个关键创新实现了跨季节、天气条件和相机设置的照片级风格迁移：首先，混合嵌入方案将持久的场景几何与瞬态风格属性分离，允许在保持结构完整性的同时进行逼真的环境编辑。其次，不确定性感知渲染减轻了扩散先验带来的监督噪声，使得在极端风格变化下也能进行稳健训练。第三，统一参数模型通过正则化更新防止几何漂移，在七个车载相机之间保持多视角一致性。我们的框架保留了原始场景的运动模式和几何关系。定性结果展示了不同条件（雪、沙尘暴、夜晚）之间合理的转换，而定量评估显示了风格迁移下最先进的几何精度。该方法为城市模拟建立了新的能力，适用于需要可靠环境一致性的自动驾驶车辆测试和增强现实系统。代码将在发表后公开提供。
英文摘要：
Urban scene reconstruction requires modeling both static infrastructure and dynamic elements while supporting diverse environmental conditions. We present \textbf{StyledStreets}, a multi-style street simulator that achieves instruction-driven scene editing with guaranteed spatial and temporal consistency. Building on a state-of-the-art Gaussian Splatting framework for street scenarios enhanced by our proposed pose optimization and multi-view training, our method enables photorealistic style transfers across seasons, weather conditions, and camera setups through three key innovations: First, a hybrid embedding scheme disentangles persistent scene geometry from transient style attributes, allowing realistic environmental edits while preserving structural integrity. Second, uncertainty-aware rendering mitigates supervision noise from diffusion priors, enabling robust training across extreme style variations. Third, a unified parametric model prevents geometric drift through regularized updates, maintaining multi-view consistency across seven vehicle-mounted cameras. Our framework preserves the original scene's motion patterns and geometric relationships. Qualitative results demonstrate plausible transitions between diverse conditions (snow, sandstorm, night), while quantitative evaluations show state-of-the-art geometric accuracy under style transfers. The approach establishes new capabilities for urban simulation, with applications in autonomous vehicle testing and augmented reality systems requiring reliable environmental consistency. Codes will be publicly available upon publication.
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