用于驾驶视频生成的逼真且可控的3D高斯引导物体编辑
Realistic and Controllable 3D Gaussian-Guided Object Editing for Driving Video Generation
AI总结：
提出G²Editor框架，利用3D高斯先验、场景级布局和分层细粒度特征，实现驾驶视频中逼真、可控的物体重定位、插入与删除。
摘要：
极端案例对于训练和验证自动驾驶系统至关重要，但从现实世界收集这些案例往往成本高昂且充满危险。在采集的传感器数据中编辑物体为生成多样化场景提供了一种有效替代方案，通常通过3D Gaussian Splatting或图像生成模型实现。然而，这些方法往往存在视觉保真度有限或姿态控制不精确的问题。为解决这些问题，我们提出G²Editor，这是一个专为驾驶视频中照片级真实且精确的物体编辑而设计的框架。我们的方法利用被编辑物体的3D高斯表示作为密集先验，并将其注入去噪过程，以确保准确的姿态控制和空间一致性。该方法采用场景级3D边界框布局来重建非目标物体的遮挡区域。此外，为引导被编辑物体的外观细节，我们在生成过程中引入分层细粒度特征作为附加条件。在Waymo Open Dataset上的实验表明，G²Editor在统一框架内有效支持物体重定位、插入和删除，在姿态可控性和视觉质量方面均优于现有方法，同时也使下游数据驱动任务受益。
英文摘要：
Corner cases are crucial for training and validating autonomous driving systems, yet collecting them from the real world is often costly and hazardous. Editing objects within captured sensor data offers an effective alternative for generating diverse scenarios, commonly achieved through 3D Gaussian Splatting or image generative models. However, these approaches often suffer from limited visual fidelity or imprecise pose control. To address these issues, we propose G^2Editor, a framework designed for photorealistic and precise object editing in driving videos. Our method leverages a 3D Gaussian representation of the edited object as a dense prior, injected into the denoising process to ensure accurate pose control and spatial consistency. A scene-level 3D bounding box layout is employed to reconstruct occluded areas of non-target objects. Furthermore, to guide the appearance details of the edited object, we incorporate hierarchical fine-grained features as additional conditions during generation. Experiments on the Waymo Open Dataset demonstrate that G^2Editor effectively supports object repositioning, insertion, and deletion within a unified framework, outperforming existing methods in both pose controllability and visual quality, while also benefiting downstream data-driven tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
