Cyclone: Diffusion Model for Cycle-Consistent Weather Editing from Unpaired Driving Data

• URL: http://arxiv.org/abs/2607.13927v1
• Date: Wed, 15 Jul 2026 15:08:45 GMT
• Title: Cyclone: Diffusion Model for Cycle-Consistent Weather Editing from Unpaired Driving Data
• Abstract summary: We present Cyclone, a unified framework for weather editing based on latent diffusion.<n>We show that our approach produces more realistic, structure-preserving outputs than existing baselines.
• Score: 6.193129698981512
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Reliable perception under diverse weather conditions remains a major challenge for autonomous driving systems. A common strategy to improve robustness is either to synthesize adverse weather conditions for training perception models or to apply weather-removal techniques to recover clean inputs. However, existing approaches typically rely on synthetic data augmentation or physics-based, task-specific models that require paired training data and often struggle to generate realistic weather effects or generalize robustly to out-of-domain scenarios. Toward this problem, we present Cyclone, a unified framework for weather editing based on latent diffusion, equipped with cycle-consistent constraints and knowledge from image-text models. Cyclone enables the generation of multiple weather conditions across diverse scenes while eliminating the need for paired data. Experimental results show that our approach produces more realistic, structure-preserving outputs than existing baselines and leads to consistent improvements across several downstream driving perception tasks. Furthermore, we demonstrate that Cyclone can be distilled to a video diffusion model for temporally consistent weather editing.

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