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

arXiv: https://arxiv.org/abs/2604.18993 · HTML: https://mlsanity.com/paper/2604.18993/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 6 references that are in the map.
- First posted on arXiv 21 Apr 2026.

## Builds on (most cited first, 3 of 3)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Perception and Sensing for Autonomous Vehicles Under Adverse Weather Conditions: A Survey](https://mlsanity.com/paper/2112.08936/) (2021)
- [SimGen: Simulator-conditioned Driving Scene Generation](https://mlsanity.com/paper/2406.09386/) (2024)

## Built on by (most cited first, 2 of 2)

- [OneFixer: High-Quality and Consistent One-Step Autoregressive 3DGS Refinement for Driving Scenes](https://mlsanity.com/paper/2609.32175/) (2026)
- [MeteoVerse: Unified Weather-Controllable Video World Model](https://mlsanity.com/paper/2609.36810/) (2026)

## Abstract (arXiv)

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