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Cyclone diffusion model enables unpaired weather editing for driving data

By PulseAugur Editorial · [3 sources] ·

Researchers have developed Cyclone, a novel framework for weather editing in images and videos using latent diffusion models. This approach, detailed in a recent arXiv paper, leverages cycle-consistent constraints and knowledge from image-text models to generate diverse weather conditions without requiring paired training data. Cyclone aims to improve the robustness of autonomous driving systems by creating more realistic, structure-preserving weather effects and has shown consistent improvements in downstream driving perception tasks. AI

IMPACT Enhances robustness of autonomous driving systems by improving perception under diverse weather conditions.

RANK_REASON The cluster contains an academic paper detailing a new model and framework.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

Cyclone diffusion model enables unpaired weather editing for driving data

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COVERAGE [3]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

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

    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 recov…

  2. arXiv cs.CV TIER_1 English(EN) · Thang-Anh-Quan Nguyen, Moussab Bennehar, Luis Guillermo Roldao Jimenez, Nathan Piasco, Dzmitry Tsishkou, Laurent Caraffa, Jean-Philippe Tarel, Roland Br\'emond ·

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

    arXiv:2607.13927v1 Announce Type: new 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…

  3. arXiv cs.CV TIER_1 English(EN) · Roland Brémond ·

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

    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 recov…

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