PSDiffusion: Symmetry-Aware Diffusion for Harmonious Portrait Cast Shadow Removal

Portrait cast shadow removal is a critical yet challenging task in computer vision and portrait editing. Current methods mainly rely on planar features, such as facial landmarks, to assist in cast shadow removal and often fail to fully exploit the geometric information of the face. Moreover, current methods often ignore the asymmetry of facial details and self-shadows caused by lighting orientations. In this work, we propose a Portrait Shadow Diffusion (PSDiffusion) framework to address these challenges. PSDiffusion combines diffusion models with facial geometry and illumination, achieving delicate balance between cast shadow removal and facial self-shadow preserving through illumination consistency constraint and symmetry-aware attention mechanism. Specifically, we first design a Face Symmetry Flip Module to generate a flipped facial image, which presents the potentially cast shadow-free features for subsequent shadow removal. Then, we introduce a pre-trained Feature Extraction Diffusion Module to extract cast shadow free features from the flipped facial image. Finally, we adaptively fuse the extracted cast shadow free features with the features from the input image into the Symmetry-aware Diffusion Module, and generate the final cast shadow removal result with preserved self-shadows. Furthermore, we construct a large-scale portrait shadow dataset (LPSD) with diverse real-world scenarios of portrait shadows. Comprehensive experiments conducted on existing open-source facial datasets and web-collected images demonstrate that our method surpasses state-of-the-art techniques in cast shadow removal.