AutoAWG: Adverse Weather Generation with Adaptive Multi-Controls for Automotive Videos
For autonomous driving researchers, AutoAWG provides a practical solution to generate realistic adverse weather videos with preserved annotations, addressing the data scarcity bottleneck.
AutoAWG introduces a controllable adverse weather video generation framework for autonomous driving that balances strong weather stylization with high-fidelity preservation of safety-critical targets. On nuScenes, it reduces FID by 50.0% and FVD by 16.1% without first-frame conditioning, and further reduces them by 8.7% and 7.2% with first-frame conditioning.
Perception robustness under adverse weather remains a critical challenge for autonomous driving, with the core bottleneck being the scarcity of real-world video data in adverse weather. Existing weather generation approaches struggle to balance visual quality and annotation reusability. We present AutoAWG, a controllable Adverse Weather video Generation framework for Autonomous driving. Our method employs a semantics-guided adaptive fusion of multiple controls to balance strong weather stylization with high-fidelity preservation of safety-critical targets; leverages a vanishing point-anchored temporal synthesis strategy to construct training sequences from static images, thereby reducing reliance on synthetic data; and adopts masked training to enhance long-horizon generation stability. On the nuScenes validation set, AutoAWG significantly outperforms prior state-of-the-art methods: without first-frame conditioning, FID and FVD are relatively reduced by 50.0% and 16.1%; with first-frame conditioning, they are further reduced by 8.7% and 7.2%, respectively. Extensive qualitative and quantitative results demonstrate advantages in style fidelity, temporal consistency, and semantic--structural integrity, underscoring the practical value of AutoAWG for improving downstream perception in autonomous driving. Our code is available at: https://github.com/higherhu/AutoAWG