Realistic and Controllable 3D Gaussian-Guided Object Editing for Driving Video Generation

• URL: http://arxiv.org/abs/2508.20471v1
• Date: Thu, 28 Aug 2025 06:39:53 GMT
• Title: Realistic and Controllable 3D Gaussian-Guided Object Editing for Driving Video Generation
• Authors: Jiusi Li, Jackson Jiang, Jinyu Miao, Miao Long, Tuopu Wen, Peijin Jia, Shengxiang Liu, Chunlei Yu, Maolin Liu, Yuzhan Cai, Kun Jiang, Mengmeng Yang, Diange Yang,
• Abstract summary: G2Editor is a framework designed for imprecise and precise object editing in driving videos.<n>A scene-level 3D bounding box layout is employed to reconstruct occluded areas of non-target objects.<n>Experiments demonstrate that G2Editor effectively supports object repositioning, insertion and deletion within a unified framework.
• Score: 12.982001613987315
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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