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.
arXiv:2610.01291v1 Announce Type: new Abstract: Recent advances in deep learning for shadow removal have significantly enhanced image quality and realism. However, most approaches rely on real-world...
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.
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.
Existing supervised and unsupervised shadow removal methods often suffer from limited generalization due to the insufficient diversity of available training datasets, while zero-shot methods tend to produce artifacts and require time-consuming test-time optimization. To address these issues, we propose FreeShadow, a training-free shadow removal method built upon pretrained diffusion models, which exploits diffusion priors for shadow removal without any training or optimization.
arXiv:2512.06174v3 Announce Type: replace Abstract: Generating realistic cast shadows for inserted foreground objects requires reasoning about scene geometry and illumination. However, most learning-...
arXiv:2608. 20107v1 Announce Type: new Abstract: Recent advances in generative video models have significantly improved visual realism in video object removal, yet evaluation protocols still focus on masked region fidelity, treating removal as local inpainting.
arXiv:2606. 06899v1 Announce Type: cross Abstract: Variations in illumination remain a major challenge for visual representation learning, as they induce substantial appearance changes both across and within environments.
arXiv:2606. 28094v1 Announce Type: cross Abstract: Real-world object removal is challenging due to two key difficulties: the target object's non-local effects, such as shadows and reflections, which are difficult to model, and the fact that user-provided masks are often inaccurate or incomplete.
arXiv:2601. 12507v2 Announce Type: replace-cross Abstract: Low-resolution remote sensing small object detection is limited by both missing visual details and the ambiguity of how details serve detection.
The paper introduces DORS, a diffusion-based framework for removing objects from dense scenes. It employs Dynamic Attention Routing, combining Instance-Filtered Attention to block misleading semantic cues from similar objects and Context-Guided Routing to preserve visual consistency. A new benchmark, DOR-Bench, is also presented, and experiments show DORS outperforms existing methods in reducing incomplete removal and duplicate artifacts.
arXiv:2502. 07531v5 Announce Type: replace-cross Abstract: Controllable image-to-video (I2V) generation transforms a reference image into a coherent video guided by user-specified control signals.
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.