WildShadowRemover: In-the-Wild Video Shadow Removal via Detail-Preserving Video Diffusion Models
Video shadow removal in the wild remains challenging due to complex illumination, diverse shadow appearances, and limited training data. WildShadowRemover adapts a pretrained video diffusion model via LoRA fine-tuning, introduces detail injection and frequency-decomposed modulation modules, and uses monocular depth priors. It outperforms existing methods in shadow removal quality and temporal consistency.
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[Submitted on 28 Jul 2026]
Title:WildShadowRemover: In-the-Wild Video Shadow Removal via Detail-Preserving Video Diffusion Models
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Abstract:Video shadow removal in the wild remains challenging due to complex illumination, diverse shadow appearances, and limited training data. Despite its importance to numerous vision and graphics applications, it remains largely unexplored in unconstrained real-world scenarios. To address this gap, we present WildShadowRemover, a framework that adapts a pretrained video diffusion model for robust video shadow removal via LoRA fine-tuning. To preserve fine image details while retaining the model's powerful generative prior, we augment the frozen VAE decoder with a detail injection module and introduce a shadow-mask-guided frequency-decomposed modulation module to selectively restore high-frequency textures while suppressing shadow artifacts. Monocular depth priors from Depth Anything 3 further provide geometry-aware guidance under challenging lighting conditions. We also construct WildShadow, a large-scale paired video shadow removal dataset and benchmark, covering diverse synthetic scenes. Extensive experiments demonstrate that our method outperforms existing approaches in shadow removal quality and temporal consistency, producing temporally coherent shadow-free videos with superior visual quality and strong generalization across challenging in-the-wild scenarios.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.26203 [cs.CV]
(or arXiv:2607.26203v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.26203
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Jiamin Xu [view email] [v1] Tue, 28 Jul 2026 19:08:26 UTC (4,852 KB)
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