Face swapping aims to transfer the identity of a source face onto a target face while preserving target-specific attributes such as pose, expression, lighting, skin tone, and makeup. However, since real ground truth for face swapping is unavailable, achieving both accurate identity transfer and high-quality attribute preservation remains challenging. Recent diffusion-based approaches attempt to improve visual fidelity through conditional inpainting on masked target images, but the masked condition removes crucial appearance cues, resulting in plausible yet misaligned attributes. To address this limitation, we propose APPLE (Attribute-Preserving Pseudo-Labeling), a fully diffusion-based teacher-student framework for attribute-preserving face swapping. Our approach introduces a teacher design to produce pseudo-labels aligned with the target attributes through (1) a conditional deblurring formulation that improves the preservation of global attributes such as skin tone and illumination, and (2) an attribute-aware inversion scheme that further enhances fine-grained attribute preservation such as makeup. APPLE conditions the student on clean pseudo-labels rather than degraded masked inputs, enabling more faithful attribute preservation. As a result, APPLE achieves state-of-the-art performance in attribute preservation while maintaining competitive identity transferability.

翻译：换脸旨在将源人脸的身份迁移至目标人脸，同时保留目标特有的属性如姿态、表情、光照、肤色和妆容。然而，由于换脸的真实标注数据不可获取，在实现精确身份迁移的同时保持高质量属性保留仍具挑战。近期基于扩散的方法尝试通过对遮罩目标图像进行条件修补来提升视觉保真度，但遮罩条件会去除关键外观线索，导致生成看似合理却属性错位的结果。为解决此限制，我们提出APPLE（保持属性的伪标记方法），一种全扩散型的教师-学生框架用于属性保持换脸。该方法通过以下两点设计教师模型来生成与目标属性对齐的伪标签：（1）条件去模糊公式，改善肤色和光照等全局属性的保留；（2）属性感知反演方案，进一步提升妆容等细粒度属性保留效果。APPLE以干净的伪标签而非退化的遮罩输入作为学生模型的条件，从而实现更忠实的属性保留。实验表明，APPLE在保持属性方面达到当前最优性能，同时保持具有竞争力的身份迁移能力。