Title: AutoAWG: Adverse Weather Generation with Adaptive Multi-Controls for Automotive Videos
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Adverse Weather Generation, Video Diffusion Model, Automotive Videos, Adaptive Selection

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 \emph{AutoAWG}, a controllable \emph{A}dverse \emph{W}eather video \emph{G}eneration framework for \emph{Auto}nomous 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, \emph{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 \emph{AutoAWG} for improving downstream perception in autonomous driving.

TL;DR: A method to transfer multi-views and arbitrarily length normal automative videos to adverse weather conditions using video diffusion model.