The paper introduces ShadowCLR, an unsupervised framework for removing shadows from images without requiring paired shadow–shadow-free data or shadow masks. By leveraging consistency across multiple shadowed observations of the same scene, the method regularizes the model to recover scene-consistent appearance while suppressing shadow-specific variations. Experiments on several benchmarks show that ShadowCLR achieves competitive or superior performance compared to existing unsupervised approaches.
By Anh-Kiet Duong, Petra Gomez-Kr\"amer, Jean-Michel Carozza
arXiv:2608. 06075v1 Announce Type: cross Abstract: Commercial vision-language models are reshaping computer vision, with visual priors broad enough to rival task-specific systems.
By Shilin Hu, Jingyi Xu, Dimitris Samaras, Hieu Le
Commercial vision-language models are reshaping computer vision, with visual priors broad enough to rival task-specific systems. This raises a natural question: do they reduce the need for classic, physics-informed low-level vision?
DiffusionShadow introduces a diffusion-based shadow caching framework for neural volume rendering, compressing many pre‑computed shadow INRs into a single diffusion model conditioned on lighting direction. The method encodes shadow coefficient volumes as shadow INRs, trains the diffusion model to predict shadow INR weights at inference, and integrates directly with standard INR renderers without extra runtime sampling. Experiments demonstrate faster rendering than traditional approaches while avoiding the large storage overhead of independent INRs, producing shadows that closely match reference results.
By Kai-Chen Tung, Qi Wu, David Bauer, Mengjiao Han, Silvio Rizzi, Kwan-Liu Ma
WildShadowRemover is a framework that adapts a pretrained video diffusion model for robust in-the-wild video shadow removal using LoRA fine-tuning. It augments the frozen VAE decoder with a detail injection module and introduces a shadow‑mask‑guided frequency‑decomposed modulation module to restore high‑frequency textures while suppressing shadow artifacts, with monocular depth priors providing geometry‑aware guidance. The authors also create WildShadow, a large‑scale paired video shadow removal dataset, and show that their method outperforms existing approaches in shadow removal quality, temporal consistency, and generalization across challenging real‑world scenarios.
By Jiamin Xu, Cong Wang, Zheng Dong, Chi Wang, Renshu Gu, Weiwei Xu, Gang Xu
arXiv:2609.00901v1 Announce Type: new
Abstract: Modifying the illumination of driving images is a fundamental challenge, as most datasets are captured at specific times of day. Existing methods rely...
By Hala Djeghim, Nathan Piasco, Luis Rold\~ao, Moussab Bennehar, Dzmitry Tsishkou, C\'eline Loscos, D\'esir\'e Sidib\'e
The paper introduces a benchmark called Shedding Light to evaluate how well generative image models understand and reproduce lighting. The benchmark tests models by asking them to inpaint a simple object, called a light probe, into real photographs and then compares the generated probe to the ground truth to assess lighting direction, colour, and radiance. The authors provide a scalable protocol and open-source code and data for systematic assessment of photometric accuracy in future models.
By Justine Giroux, Jack Oliver Hilliard, Yannick Hold-Geoffroy, Javier Vazquez-Corral, Jean-Fran\c{c}ois Lalonde
arXiv:2512. 17504v2 Announce Type: replace-cross Abstract: Recent advances in diffusion models have enabled impressive video editing capabilities, yet production-grade Video Object Insertion (VOI) remains challenging due to inadequate 4D scene understanding and a lack of proper optical interactions, such as shadows and reflections.
By Hoiyeong Jin, Hyojin Jang, Junha Hyung, Jeongho Kim, Kinam Kim, Dongjin Kim, Huijin Choi, Hyeonji Kim, Jaegul Choo
arXiv:2609.28300v1 Announce Type: new
Abstract: Ill-posed inverse problems require priors to constrain the solution space toward plausible outcomes. In inverse rendering, learned priors modeling the...
By Andreea Ardelean, Bernhard Egger
The paper introduces AgenticShadow, a new dataset of 17,138 image‑mask‑target triplets created through an offline agentic workflow that combines physics‑motivated generation, failure detection, feedback‑driven retry, candidate selection, and deterministic correction. This approach addresses the long‑standing lack of diverse paired shadow‑free training data by leveraging existing shadow detection datasets and producing realistic shadow‑free targets. Models trained on AgenticShadow show significant improvements, reducing color distribution differences by 50.5% and cross‑domain LAB RMSE by 19.7‑37.5% compared to prior work.
By Shilin Hu, Jingyi Xu, Dimitris Samaras, Hieu Le
arXiv:2609.10363v1 Announce Type: new
Abstract: SceneHI is a framework that lifts high-resolution, illumination-aware priors from 2D diffusion models to perform 3D texture synthesis. It is the first...
By Athanasios Tragakis, Marco Aversa, Daniela Ivanova, Chaitanya Kaul, Roderick Murray-Smith, Daniele Faccio, Paul Henderson
arXiv:2609.37951v1 Announce Type: new
Abstract: Replacing a visible advertisement in a broadcast frame is geometrically straightforward but photometrically delicate. A pasted graphic can have the cor...
By Rameshwar Mishra, Bishshoy Das, A. V. Subramanyam, Guan-Ming Su