High-fidelity and controllable 3D simulation is essential for addressing the long-tail data scarcity in Autonomous Driving (AD), yet existing methods struggle to simultaneously achieve photorealistic rendering and interactive traffic editing. Current approaches often falter in large-angle novel view synthesis and suffer from geometric or lighting artifacts during asset manipulation. To address these challenges, we propose SymDrive, a unified diffusion-based framework capable of joint high-quality rendering and scene editing. We introduce a Symmetric Auto-regressive Online Restoration paradigm, which constructs paired symmetric views to recover fine-grained details via a ground-truth-guided dual-view formulation and utilizes an auto-regressive strategy for consistent lateral view generation. Furthermore, we leverage this restoration capability to enable a training-free harmonization mechanism, treating vehicle insertion as context-aware inpainting to ensure seamless lighting and shadow consistency. Extensive experiments demonstrate that SymDrive achieves state-of-the-art performance in both novel-view enhancement and realistic 3D vehicle insertion.
@article{arxiv.2512.21618,
title = {SymDrive: Realistic and Controllable Driving Simulator via Symmetric Auto-regressive Online Restoration},
author = {Zhiyuan Liu and Daocheng Fu and Pinlong Cai and Lening Wang and Ying Liu and Yilong Ren and Botian Shi and Jianqiang Wang},
journal= {arXiv preprint arXiv:2512.21618},
year = {2025}
}