Face swapping aims to generate swapped images that fuse the identity of source faces and the attributes of target faces. Most existing works address this challenging task through 3D modelling or generation using generative adversarial networks (GANs), but 3D modelling suffers from limited reconstruction accuracy and GANs often struggle in preserving subtle yet important identity details of source faces (e.g., skin colors, face features) and structural attributes of target faces (e.g., face shapes, facial expressions). This paper presents Face Transformer, a novel face swapping network that can accurately preserve source identities and target attributes simultaneously in the swapped face images. We introduce a transformer network for the face swapping task, which learns high-quality semantic-aware correspondence between source and target faces and maps identity features of source faces to the corresponding region in target faces. The high-quality semantic-aware correspondence enables smooth and accurate transfer of source identity information with minimal modification of target shapes and expressions. In addition, our Face Transformer incorporates a multi-scale transformation mechanism for preserving the rich fine facial details. Extensive experiments show that our Face Transformer achieves superior face swapping performance qualitatively and quantitatively.

翻译：人脸交换旨在生成融合源人脸身份属性与目标人脸属性特征的交换图像。现有研究主要通过三维建模或生成对抗网络（GANs）解决这一挑战性任务，但三维建模受限于重建精度不足，而GANs往往难以保留源人脸微妙而关键的身份细节（如肤色、面部特征）及目标人脸的结构属性（如脸型、表情）。本文提出面部变换器——一种新型人脸交换网络，能够在交换人脸图像中同时精确保留源身份与目标属性。我们引入变换器网络完成人脸交换任务，该网络学习源人脸与目标人脸间的高质量语义感知对应关系，将源人脸的身份特征映射至目标人脸的对应区域。这种高质量语义感知对应关系能够在最小化修改目标脸型与表情的前提下，实现源身份信息的平滑精确传递。此外，我们的面部变换器融合多尺度变换机制以保留丰富的精细面部细节。大量实验表明，所提出的面部变换器在定性及定量评估中均展现出卓越的人脸交换性能。