Abstract
Controllable driving scene generation is critical for realistic and scalable autonomous driving simulation, yet existing approaches struggle to jointly achieve photorealism and precise control. We introduce HorizonForge, a unified framework that reconstructs scenes as editable Gaussian Splats and Meshes, enabling fine-grained 3D manipulation and language-driven vehicle insertion. Edits are rendered through a noise-aware video diffusion process that enforces spatial and temporal consistency, producing diverse scene variations in a single feed-forward pass without per-trajectory optimization. To standardize evaluation, we further propose HorizonSuite, a comprehensive benchmark spanning ego- and agent-level editing tasks such as trajectory modifications and object manipulation. Extensive experiments show that Gaussian-Mesh representation delivers substantially higher fidelity than alternative 3D representations, and that temporal priors from video diffusion are essential for coherent synthesis. Combining these findings, HorizonForge establishes a simple yet powerful paradigm for photorealistic, controllable driving simulation, achieving an 83.4% user-preference gain and a 25.19% FID improvement over the second best state-of-the-art method. Project page: this https URL .
Abstract (translated)
可控的驾驶场景生成对于现实主义和可扩展性的自主驾驶仿真至关重要，然而现有的方法难以同时实现照片级真实感与精确控制。我们引入了HorizonForge，这是一个统一框架，它将场景重建为可编辑的高斯斑点（Gaussian Splats）和网格（Meshes），从而能够进行细致入微的三维操作，并通过语言驱动的方式插入车辆。这些修改通过一个噪声感知视频扩散过程来渲染，在该过程中强制执行空间和时间一致性，无需每个轨迹优化即可在一次前馈传递中生成多样化的场景变化。 为了标准化评估，我们进一步提出了HorizonSuite，这是一个全面的基准测试平台，涵盖了自我主体级别（ego-level）和代理级别(agent-level)的任务编辑任务，例如轨迹修改和物体操作。广泛的实验表明，高斯网格表示法比其他三维表示方法具有显著更高的保真度，并且来自视频扩散的时间先验对于连贯合成是必不可少的。 结合这些发现，HorizonForge确立了一个简单但强大的范式，用于实现逼真的、可控制的驾驶仿真，在用户偏好和FID（Fréchet Inception Distance）指标上分别比现有最佳方法高出83.4%和25.19%。项目页面：[此链接](https://this-url.com)。 请注意，最后一个句子中的链接需要您替换为实际项目网页的URL。
URL
https://arxiv.org/abs/2602.21333
