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: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...
By Junseong Shin, Kijun Kim, Minseong Kim, Dongjin Kim, Tae Hyun Kim
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
MAST (Mask‑Guided Attention Control for Training‑Free Regional‑Multi Style Transfer) is a framework that enables diffusion models to apply multiple reference styles to user‑specified regions of a content image without any training or optimization. It introduces logit‑level attention mass allocation, sharpness‑aware temperature scaling, and discrepancy‑aware detail injection to address mass allocation, selectivity, and detail loss problems in regional‑multi style transfer. Experiments with two to five styles show that MAST outperforms baselines in ArtFID, FID, and R‑FID, achieving high regional style fidelity, content preservation, and scalability.
By Dongkyung Kang, Jaeyeon Hwang, Junseo Park, Minji Kang, Yeryeong Lee, Beomseok Ko, Hanyoung Roh, Jeongmin Shin, Hyeryung Jang
arXiv:2609.01123v1 Announce Type: new
Abstract: Recent advancements in low-light image enhancement have leveraged diffusion models for their strong ability to generate perceptually realistic, detaile...
By Ruoyu Guo, Haonan Zhong, Maurice Pagnucco, Yang Song
arXiv:2605. 31162v1 Announce Type: cross Abstract: Unconditional diffusion models offer powerful generative priors, yet steering them toward aesthetically enhanced outputs remains largely unexplored.
By Shreyansh Modi, Akshat Tomar, Aarush Aggarwal