Facial shadows often degrade image quality and the performance of vision algorithms. Existing methods struggle to remove shadows while preserving texture, especially under complex lighting conditions, and they lack real-world paired datasets for training. We present the Augmented Shadow Face in the Wild (ASFW) dataset, the first large-scale real-world dataset for facial shadow removal, containing 1,081 paired shadow and shadow-free images created via a professional Photoshop workflow. ASFW offers photorealistic shadow variations and accurate ground truths, bridging the gap between synthetic and real domains. Deep models trained on ASFW demonstrate improved shadow removal in real-world conditions. We also introduce the Face Shadow Eraser (FSE) method to showcase the effectiveness of the dataset. Experiments demonstrate that ASFW enhances the performance of facial shadow removal models, setting new standards for this task.

翻译：面部阴影通常会降低图像质量并影响视觉算法的性能。现有方法在去除阴影的同时难以保持纹理细节，尤其在复杂光照条件下，且缺乏用于训练的真实世界配对数据集。我们提出了野外增强阴影人脸（ASFW）数据集，这是首个用于面部阴影去除的大规模真实世界数据集，包含通过专业Photoshop工作流程创建的1,081对阴影图像与无阴影图像。ASFW提供逼真的阴影变化和精确的真实标注，弥合了合成域与真实域之间的差距。在ASFW上训练的深度模型在真实场景中展现出更优的阴影去除效果。我们还提出了面部阴影擦除器（FSE）方法以验证数据集的有效性。实验表明，ASFW显著提升了面部阴影去除模型的性能，为该任务树立了新的标准。