提出无需先验的不确定性引导人脸抠图方法，提升遮挡下滤镜效果质量。
Uncertainty-Guided Face Matting for Occlusion-Aware Face Transformation
- 用不确定性指导知识蒸馏，聚焦模糊遮挡区域
- 在多个基准上超越现有方法，显著提升遮挡场景表现
- 适合实时视频应用，无需额外输入如分割掩码
人脸滤镜已成为短视频内容的核心元素，支持风格化和人脸替换等多种视觉特效。然而，在手、头发或配饰等遮挡物干扰时，其性能通常下降。为此，本文提出人脸抠图新任务，通过估计细粒度透明度贴图（alpha matte）实现遮挡物与人脸区域分离。我们提出FaceMat框架，无需trimap，具备不确定性感知能力，可在复杂遮挡下生成高质量alpha matte。该方法采用两阶段训练：教师模型使用负对数似然（NLL）损失联合预测alpha matte与像素级不确定性；学生模型则通过空间自适应知识蒸馏，聚焦于不确定或遮挡区域，提升泛化性与语义一致性。不同于依赖trimap或分割掩码的现有方法，本框架无需辅助输入，适用于实时应用。此外，我们重新定义抠图目标，将皮肤明确设为前景，遮挡物为背景，提升合成清晰度。为支持该任务，我们构建了大规模合成数据集CelebAMat。大量实验表明，FaceMat在多个基准上优于现有方法，显著增强真实无约束视频中人脸滤镜的视觉质量与鲁棒性。源代码与数据集已开源。
原文摘要 · Abstract (English)
Face filters have become a key element of short-form video content, enabling a wide array of visual effects such as stylization and face swapping. However, their performance often degrades in the presence of occlusions, where objects like hands, hair, or accessories obscure the face. To address this limitation, we introduce the novel task of face matting, which estimates fine-grained alpha mattes to separate occluding elements from facial regions. We further present FaceMat, a trimap-free, uncertainty-aware framework that predicts high-quality alpha mattes under complex occlusions. Our approach leverages a two-stage training pipeline: a teacher model is trained to jointly estimate alpha mattes and per-pixel uncertainty using a negative log-likelihood (NLL) loss, and this uncertainty is then used to guide the student model through spatially adaptive knowledge distillation. This formulation enables the student to focus on ambiguous or occluded regions, improving generalization and preserving semantic consistency. Unlike previous approaches that rely on trimaps or segmentation masks, our framework requires no auxiliary inputs making it well-suited for real-time applications. In addition, we reformulate the matting objective by explicitly treating skin as foreground and occlusions as background, enabling clearer compositing strategies. To support this task, we newly constructed CelebAMat, a large-scale synthetic dataset specifically designed for occlusion-aware face matting. Extensive experiments show that FaceMat outperforms state-of-the-art methods across multiple benchmarks, enhancing the visual quality and robustness of face filters in real-world, unconstrained video scenarios. The source code and CelebAMat dataset are available at https://github.com/hyebin-c/FaceMat.git
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