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. 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-frequ...
| Authors | Jiamin Xu, Cong Wang, Zheng Dong et al. |
|---|---|
| Primary category | cs.CV |
| Published | 28 July 2026 |
| Last updated at source | 22 August 2026 |
Models and datasets referencing this paper
- dcowg/wildshadow-imageDataset
- dcowg/wildshadow-videoDataset
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