AutoAWG: Adverse Weather Generation with Adaptive Multi-Controls for Automotive Videos

• URL: http://arxiv.org/abs/2604.18993v1
• Date: Tue, 21 Apr 2026 02:24:49 GMT
• Title: AutoAWG: Adverse Weather Generation with Adaptive Multi-Controls for Automotive Videos
• Abstract summary: We present AutoAWG, a controllable Adverse Weather video Generation framework for Autonomous driving.<n>Our method employs a semantics-guided adaptive fusion of multiple controls to balance strong weather stylization with high-fidelity preservation of safety-critical targets.<n>Our results demonstrate advantages in style fidelity, temporal consistency, and semantic--structural integrity.
• Score: 7.720806181437183
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

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