# DrivingGaussian++: Towards Realistic Reconstruction and Editable Simulation for Surrounding Dynamic Driving Scenes

**Authors:** Yajiao Xiong, Xiaoyu Zhou, Yongtao Wan, Deqing Sun, Ming-Hsuan Yang

arXiv: 2508.20965 · 2025-08-29

## TL;DR

DrivingGaussian++ is a novel framework that enables realistic reconstruction and controllable editing of dynamic driving scenes, integrating scene modeling, editing capabilities, and LLM-based motion generation for enhanced scene diversity.

## Contribution

It introduces a new scene modeling approach combining static and dynamic Gaussian representations with LiDAR priors, enabling realistic reconstruction and editing without training.

## Key findings

- Outperforms existing methods in scene reconstruction and synthesis
- Supports training-free controllable editing including texture, weather, and object manipulation
- Generates diverse, realistic multi-view driving scenarios

## Abstract

We present DrivingGaussian++, an efficient and effective framework for realistic reconstructing and controllable editing of surrounding dynamic autonomous driving scenes. DrivingGaussian++ models the static background using incremental 3D Gaussians and reconstructs moving objects with a composite dynamic Gaussian graph, ensuring accurate positions and occlusions. By integrating a LiDAR prior, it achieves detailed and consistent scene reconstruction, outperforming existing methods in dynamic scene reconstruction and photorealistic surround-view synthesis. DrivingGaussian++ supports training-free controllable editing for dynamic driving scenes, including texture modification, weather simulation, and object manipulation, leveraging multi-view images and depth priors. By integrating large language models (LLMs) and controllable editing, our method can automatically generate dynamic object motion trajectories and enhance their realism during the optimization process. DrivingGaussian++ demonstrates consistent and realistic editing results and generates dynamic multi-view driving scenarios, while significantly enhancing scene diversity. More results and code can be found at the project site: https://xiong-creator.github.io/DrivingGaussian_plus.github.io

## Full text

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## Figures

22 figures with captions in the complete paper: https://tomesphere.com/paper/2508.20965/full.md

## References

70 references — full list in the complete paper: https://tomesphere.com/paper/2508.20965/full.md

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Source: https://tomesphere.com/paper/2508.20965