首个大规模真实人脸阴影去除数据集，提升去影效果与真实感
Beyond Shadows: A Large-Scale Benchmark and Multi-Stage Framework for High-Fidelity Facial Shadow Removal
- 构建1081对真实人脸阴影/无影图像，专业PS流程生成
- 基于ASFW训练模型，在复杂光照下去影更精准
- 适合图像修复、人脸处理领域研究者使用
人脸阴影会降低图像质量并影响视觉算法性能。现有方法在复杂光照条件下难以同时去除阴影并保留纹理，且缺乏真实世界配对数据集用于训练。本文提出Augmented Shadow Face in the Wild（ASFW）数据集，是首个大规模真实场景人脸阴影去除数据集，包含1,081对通过专业Photoshop流程生成的阴影与无影图像，具备高度逼真的阴影变化和精确的真值标签，弥合了合成与真实数据之间的差距。基于ASFW训练的深度模型在真实条件下展现出更优的去影能力。同时提出Face Shadow Eraser（FSE）方法以验证数据集有效性。实验表明，ASFW显著提升了面部阴影去除模型的性能，为该任务设立了新标准。
原文摘要 · Abstract (English)
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.
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