Detail-Preserving Latent Diffusion for Stable Shadow Removal

TL;DR

This paper introduces a two-stage fine-tuning approach leveraging a pre-trained Stable Diffusion model to achieve high-quality, detail-preserving shadow removal with strong generalization across diverse scenes.

Contribution

It proposes a novel two-stage fine-tuning pipeline that enhances shadow removal quality and detail preservation while improving cross-dataset generalization.

Findings

Outperforms state-of-the-art shadow removal methods

Demonstrates strong generalization to unseen datasets

Effectively preserves high-frequency details in results

Abstract

Achieving high-quality shadow removal with strong generalizability is challenging in scenes with complex global illumination. Due to the limited diversity in shadow removal datasets, current methods are prone to overfitting training data, often leading to reduced performance on unseen cases. To address this, we leverage the rich visual priors of a pre-trained Stable Diffusion (SD) model and propose a two-stage fine-tuning pipeline to adapt the SD model for stable and efficient shadow removal. In the first stage, we fix the VAE and fine-tune the denoiser in latent space, which yields substantial shadow removal but may lose some high-frequency details. To resolve this, we introduce a second stage, called the detail injection stage. This stage selectively extracts features from the VAE encoder to modulate the decoder, injecting fine details into the final results. Experimental results show that our method outperforms state-of-the-art shadow removal techniques. The cross-dataset evaluation further demonstrates that our method generalizes effectively to unseen data, enhancing the applicability of shadow removal methods.