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. In addition, recent diffusion-based approaches attempt to improve visual fidelity through conditional inpainting on masked target images, but the masked condition removes crucial appearance cues of target, resulting in plausible yet misaligned attributes. To address these limitations, we propose APPLE (Attribute-Preserving Pseudo-Labeling), a diffusion-based teacher-student framework that enhances attribute fidelity through attribute-aware pseudo-label supervision. We reformulate face swapping as a conditional deblurring task to more faithfully preserve target-specific attributes such as lighting, skin tone, and makeup. In addition, we introduce an attribute-aware inversion scheme to further improve detailed attribute preservation. Through an elaborate attribute-preserving design for teacher learning, APPLE produces high-quality pseudo triplets that explicitly provide the student with direct face-swapping supervision. Overall, APPLE achieves state-of-the-art performance in terms of attribute preservation and identity transfer, producing more photorealistic and target-faithful results.

翻译：人脸交换旨在将源人脸的身份信息转移到目标人脸上，同时保持目标人脸特有的属性，如姿态、表情、光照、肤色和妆容。然而，由于缺乏真实的人脸交换标注数据，同时实现准确的身份转移与高质量的属性保持仍然具有挑战性。此外，近期基于扩散模型的方法试图通过对掩码目标图像进行条件修复来提升视觉保真度，但掩码条件会移除目标人脸的关键外观线索，导致生成属性看似合理却与目标未对齐。为解决这些局限性，我们提出了APPLE（属性保持伪标签），一种基于扩散模型的师生框架，通过属性感知的伪标签监督来增强属性保真度。我们将人脸交换重新表述为一个条件去模糊任务，以更忠实地保持目标特有的属性，如光照、肤色和妆容。此外，我们引入了一种属性感知的反演方案，以进一步提升细节属性的保持效果。通过对教师学习过程进行精心的属性保持设计，APPLE能够生成高质量的伪三元组，为学生模型提供明确且直接的人脸交换监督。总体而言，APPLE在属性保持和身份转移方面实现了最先进的性能，生成了更具照片真实感且更忠实于目标人脸的结果。