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arxiv: 2508.06982 · v7 · submitted 2025-08-09 · 💻 cs.CV · cs.AI

IntrinsicWeather: Controllable Weather Editing in Intrinsic Space

Yixin Zhu , Zuo-Liang Zhu , Jian Yang , Milo\v{s} Ha\v{s}an , Jin Xie , Beibei Wang This is my paper

Pith reviewed 2026-05-18 23:55 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords intrinsic decompositionweather editingdiffusion modelscontrollable image synthesisscene renderingautonomous driving
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The pith

Decomposing an image into intrinsic maps of geometry, materials, and lighting allows text-guided weather changes with greater consistency than pixel editing.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces a diffusion-based system that first recovers the physical properties of a scene from a single photo as intrinsic maps. A forward rendering stage then uses these maps together with a text description of desired weather to produce the edited image. Working in this separated space improves spatial control and reduces the inconsistencies that appear when weather is altered directly in pixel space. The method includes an attention mechanism tailored to intrinsic maps for better handling of large outdoor scenes. New synthetic and real datasets with intrinsic annotations are released to train and evaluate the components.

Core claim

An inverse renderer based on diffusion priors estimates material properties, scene geometry, and lighting as intrinsic maps from an input image; a forward renderer then combines these maps with CLIP-space interpolated weather prompts to generate the output image, achieving higher controllability and realism than direct pixel-space editing.

What carries the argument

The pair of diffusion-based inverse and forward renderers that operate on estimated intrinsic maps of material properties, scene geometry, and lighting to separate physical scene content from weather appearance.

If this is right

  • The approach outperforms existing pixel-space weather editing, weather restoration, and rendering-based methods on standard benchmarks.
  • Detection and segmentation models trained or tested on the edited images show increased robustness under challenging weather.
  • CLIP-space interpolation of weather prompts produces fine-grained control over the strength and type of weather effects.
  • The intrinsic map-aware attention improves decomposition quality for large outdoor scenes.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same intrinsic decomposition could support consistent edits of other appearance factors such as time of day or season without retraining the core models.
  • Generated pairs of original and weather-altered images with shared intrinsics could serve as training data to improve weather robustness in downstream vision systems.
  • If the recovered intrinsics prove stable across multiple edits, the framework may extend to iterative scene manipulations while preserving 3D consistency.

Load-bearing premise

The intrinsic maps recovered from one photograph contain all the information needed to render the same scene under any new weather condition without creating geometric or material errors.

What would settle it

A side-by-side comparison in which images edited by the method are fed into a 3D reconstruction pipeline and produce larger geometric errors than images edited by pixel-space baselines.

Figures

Figures reproduced from arXiv: 2508.06982 by Beibei Wang, Jian Yang, Jin Xie, Milo\v{s} Ha\v{s}an, Yixin Zhu, Zuo-Liang Zhu.

Figure 1
Figure 1. Figure 1: By leveraging a latent diffusion model, combined with explicit visual guidance based on intrinsic maps, our method achieves high-quality results in [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Existing methods struggle to process different weather. The first [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: An overview of our method. We propose a weather-guided diffusion for FR and IR. The IR diffusion decomposes images into intrinsic maps, including a [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: Example of our WeatherSynthetic. Each row shows a scene rendered [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
Figure 4
Figure 4. Figure 4: Intrinsic map-aware attention visualization. We reveal the heatmaps [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: Ablation study on MAA. different regions of the image. Our MAA provides such guidance for diffusion to help improve the quality of inverse rendering. We train an IR diffusion model without MAA, replacing the visual condition with original text guidance. As shown in Tab. 2, for both indoor scene and AD scene, the model without MAA behaves poorly than our full model. We show a qualitative result in [PITH_FU… view at source ↗
Figure 7
Figure 7. Figure 7: A typical failure case on a foggy scene. WeatherDiffusion fails to [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Qualitative comparison of inverse rendering on a synthetic autonomous driving scene with various weather. The highest PSNR is marked in bold. We [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Comparison of forward rendering results between WeatherDiffusion and RGB [PITH_FULL_IMAGE:figures/full_fig_p009_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Comparison of inverse rendering between our method and others on real data with various weather conditions. [PITH_FULL_IMAGE:figures/full_fig_p010_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Forward rendering on real data with different weather conditions. The first image is the original image, and the following is the re-rendered image by [PITH_FULL_IMAGE:figures/full_fig_p010_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Our WeatherDiffusion helps the segmentation and detection models improve their performance. Segformer [Xie et al [PITH_FULL_IMAGE:figures/full_fig_p010_12.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper proposes IntrinsicWeather, a diffusion-based framework for controllable weather editing in intrinsic space. It consists of an inverse renderer that estimates intrinsic maps (material properties, scene geometry, and lighting) from a single input image, and a forward renderer that uses these maps along with text prompts describing weather conditions to generate the edited image. The method introduces an intrinsic map-aware attention mechanism to improve spatial correspondence in large outdoor scenes and uses CLIP-space interpolation for fine-grained weather control. New synthetic (38k images) and real-world (18k images) datasets with intrinsic map annotations are introduced. The paper claims that IntrinsicWeather outperforms state-of-the-art pixel-space editing, weather restoration, and rendering-based methods, with potential applications in enhancing robustness for autonomous driving tasks.

Significance. If the central claims hold, the work offers a meaningful advance in controllable image editing by shifting from pixel-space diffusion to intrinsic-space rendering, which could enable more physically consistent weather edits and improve synthetic data generation for vision systems operating in adverse conditions.

major comments (3)
  1. [§3] §3 (Method, inverse renderer): Single-image intrinsic decomposition for large outdoor scenes remains ill-posed; the manuscript must supply quantitative metrics (e.g., normal angular error, depth RMSE, albedo consistency) on the 18k real-world dataset to demonstrate that the estimated maps are accurate enough to support artifact-free forward rendering of arbitrary weather without geometric or material inconsistencies.
  2. [§4] §4 (Experiments): The abstract asserts outperformance over pixel-space, restoration, and rendering baselines, yet the reported results lack detailed quantitative tables, ablation studies on the intrinsic map-aware attention and CLIP interpolation components, and error analysis; without these the central empirical claim cannot be verified.
  3. [Forward renderer] Forward renderer description: The claim that CLIP-space prompt interpolation yields fine-grained, consistent weather control rests on the transfer from synthetic annotations to real images; a direct comparison of rendering artifacts or downstream detection/segmentation degradation on real adverse-weather images is needed to substantiate superiority over rendering-based methods.
minor comments (2)
  1. [Abstract] Abstract: Include one or two key quantitative metrics (e.g., FID or user-study scores) to support the outperformance statement.
  2. [Datasets] Dataset description: Clarify the exact annotation process and coverage of weather types in both the 38k synthetic and 18k real datasets.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major comment point-by-point below, indicating the revisions we will make to strengthen the paper.

read point-by-point responses

  1. Referee: [§3] §3 (Method, inverse renderer): Single-image intrinsic decomposition for large outdoor scenes remains ill-posed; the manuscript must supply quantitative metrics (e.g., normal angular error, depth RMSE, albedo consistency) on the 18k real-world dataset to demonstrate that the estimated maps are accurate enough to support artifact-free forward rendering of arbitrary weather without geometric or material inconsistencies.

    Authors: We acknowledge that single-image intrinsic decomposition is an ill-posed problem, especially for large outdoor scenes. Our inverse renderer is trained primarily on the synthetic dataset where ground-truth intrinsic maps are available, allowing quantitative evaluation there. The real-world dataset provides intrinsic map annotations, which we will use to compute and report the suggested metrics, including normal angular error, depth RMSE, and albedo consistency. These will be added to the revised manuscript to validate the map accuracy for forward rendering. revision: yes

  2. Referee: [§4] §4 (Experiments): The abstract asserts outperformance over pixel-space, restoration, and rendering baselines, yet the reported results lack detailed quantitative tables, ablation studies on the intrinsic map-aware attention and CLIP interpolation components, and error analysis; without these the central empirical claim cannot be verified.

    Authors: We agree that more comprehensive experimental results are necessary to substantiate the claims. In the revised version, we will expand Section 4 with detailed quantitative tables reporting metrics such as PSNR, SSIM, LPIPS, and FID against all baselines. We will also include ablation studies specifically on the intrinsic map-aware attention mechanism and the CLIP-space prompt interpolation, along with an error analysis discussing limitations and failure cases. revision: yes

  3. Referee: [Forward renderer] Forward renderer description: The claim that CLIP-space prompt interpolation yields fine-grained, consistent weather control rests on the transfer from synthetic annotations to real images; a direct comparison of rendering artifacts or downstream detection/segmentation degradation on real adverse-weather images is needed to substantiate superiority over rendering-based methods.

    Authors: The CLIP-space interpolation allows for smooth transitions between weather conditions by operating in the embedding space, which we demonstrate through qualitative and some quantitative results on both datasets. To further support the transfer to real images and superiority, we will add direct comparisons of rendering artifacts on real adverse-weather images. Additionally, we will include evaluations of downstream tasks like object detection and semantic segmentation on the edited real images, measuring performance changes compared to rendering-based baselines. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical pipeline with independent validation

full rationale

The paper introduces a new diffusion-based pipeline with an inverse renderer for intrinsic map estimation and a forward renderer for weather editing, supported by newly introduced synthetic (38k) and real-world (18k) datasets with intrinsic annotations. Performance claims rest on experimental comparisons against pixel-space, restoration, and rendering baselines rather than any closed-form derivation, fitted parameter renamed as prediction, or self-citation chain that reduces the central result to its own inputs by construction. The intrinsic map-aware attention and CLIP interpolation are presented as architectural choices evaluated empirically, with no equations or uniqueness theorems shown to be tautological or imported solely from overlapping prior work.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The framework rests on the domain assumption that diffusion models can serve as effective priors for both inverse estimation of intrinsic maps and forward weather-conditioned rendering; no free parameters or invented entities are explicitly introduced in the abstract.

axioms (2)
  • domain assumption Diffusion priors are suitable for estimating material, geometry, and lighting maps from single images.
    The inverse renderer component depends on this assumption to produce usable intrinsic maps.
  • domain assumption Intrinsic maps plus text prompts suffice to control realistic weather rendering without additional scene-specific calibration.
    The forward renderer and controllability claims rely on this premise.

pith-pipeline@v0.9.0 · 5722 in / 1390 out tokens · 41067 ms · 2026-05-18T23:55:05.534199+00:00 · methodology

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    Relation between the paper passage and the cited Recognition theorem.

    We propose WeatherDiffusion, a diffusion-based framework for controllable weather editing in intrinsic space... inverse renderer that estimates material properties, scene geometry, and lighting as intrinsic maps... forward renderer that utilizes these geometry and material maps along with a text prompt...

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Reference graph

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