Face Transformer：迈向高保真与高准确度的人脸交换
Face Transformer: Towards High Fidelity and Accurate Face Swapping
AI总结：
本文提出Face Transformer，通过引入transformer网络学习源与目标人脸的语义感知对应关系，并结合多尺度转换机制，在最小修改目标形状和表情的同时实现高保真与高准确度的人脸交换。
摘要：
人脸交换旨在生成融合源人脸身份和目标人脸属性的交换图像。大多数现有工作通过3D建模或使用生成对抗网络（GANs）生成来解决这一具有挑战性的任务，但3D建模受限于重建精度有限，而GANs通常难以保留源人脸微妙但重要的身份细节（例如肤色、面部特征）以及目标人脸的结构属性（例如脸型、面部表情）。本文提出Face Transformer，一种新型的人脸交换网络，能够在交换的人脸图像中同时准确保留源身份和目标属性。我们为该人脸交换任务引入了transformer网络，该网络学习源人脸与目标人脸之间高质量的语义感知对应关系，并将源人脸的身份特征映射到目标人脸的相应区域。高质量的语义感知对应关系使得源身份信息能够平滑且准确地转移，同时对目标形状和表情进行最小程度的修改。此外，我们的Face Transformer结合了多尺度转换机制，以保留丰富的精细面部细节。大量实验表明，我们的Face Transformer在定性和定量上均实现了卓越的人脸交换性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
