SymDrive: Realistic and Controllable Driving Simulator via Symmetric Auto-regressive Online Restoration
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914220374753280 |
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| author | Liu, Zhiyuan Fu, Daocheng Cai, Pinlong Wang, Lening Liu, Ying Ren, Yilong Shi, Botian Wang, Jianqiang |
| author_facet | Liu, Zhiyuan Fu, Daocheng Cai, Pinlong Wang, Lening Liu, Ying Ren, Yilong Shi, Botian Wang, Jianqiang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_21618 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SymDrive: Realistic and Controllable Driving Simulator via Symmetric Auto-regressive Online Restoration Liu, Zhiyuan Fu, Daocheng Cai, Pinlong Wang, Lening Liu, Ying Ren, Yilong Shi, Botian Wang, Jianqiang Computer Vision and Pattern Recognition Robotics 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. |
| title | SymDrive: Realistic and Controllable Driving Simulator via Symmetric Auto-regressive Online Restoration |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2512.21618 |