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arXiv · 2508.06982

IntrinsicWeather: Controllable Weather Editing in Intrinsic Space

Yixin Zhu·Zuo-Liang Zhu·Jian Yang·Miloš Hašan·Jin Xie·Beibei Wang
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Abstract

We present IntrinsicWeather, a diffusion-based framework for controllable weather editing in intrinsic space. Our framework includes two components based on diffusion priors: an inverse renderer that estimates material properties, scene geometry, and lighting as intrinsic maps from an input image, and a forward renderer that utilizes these geometry and material maps along with a text prompt that describes specific weather conditions to generate a final image. The intrinsic maps enhance controllability compared to traditional pixel-space editing approaches. We propose an intrinsic map-aware attention mechanism that improves spatial correspondence and decomposition quality in large outdoor scenes. For forward rendering, we leverage CLIP-space interpolation of weather prompts to achieve fine-grained weather control. We also introduce a synthetic and a real-world dataset, containing 38k and 18k images under various weather conditions, each with intrinsic map annotations. IntrinsicWeather outperforms state-of-the-art pixel-space editing approaches, weather restoration methods, and rendering-based methods, showing promise for downstream tasks such as autonomous driving, enhancing the robustness of detection and segmentation in challenging weather scenarios.

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Yixin Zhu, Zuo-Liang Zhu, Jian Yang, Miloš Hašan, Jin Xie, Beibei Wang. 2025-08-09. IntrinsicWeather: Controllable Weather Editing in Intrinsic Space. https://arxiv.org/abs/2508.06982

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