Face swapping has gained significant traction, driven by the plethora of
human face synthesis facilitated by deep learning methods. However, previous
face swapping methods that used generative adversarial networks (GANs) as
backbones have faced challenges such as inconsistency in blending, distortions,
artifacts, and issues with training stability. To address these limitations, we
propose an innovative end-to-end framework for high-fidelity face swapping.
First, we introduce a StyleGAN-based facial attributes encoder that extracts
essential features from faces and inverts them into a latent style code,
encapsulating indispensable facial attributes for successful face swapping.
Second, we introduce an attention-based style blending module to effectively
transfer Face IDs from source to target. To ensure accurate and quality
transferring, a series of constraint measures including contrastive face ID
learning, facial landmark alignment, and dual swap consistency is implemented.
Finally, the blended style code is translated back to the image space via the
style decoder, which is of high training stability and generative capability.
Extensive experiments on the CelebA-HQ dataset highlight the superior visual
quality of generated images from our face-swapping methodology when compared to
other state-of-the-art methods, and the effectiveness of each proposed module.
Source code and weights will be publicly available.

通过一个创新的端到端框架，我们提出了一种高保真度的面部交换方法，其中使用了深度学习方法中的生成对抗网络（GANs）作为基础，通过面部属性编码器、基于注意力的样式混合模块和样式解码器等多个模块，解决了以往面部交换方法中的混合不一致、失真、伪影和训练稳定性等问题。我们在 CelebA-HQ 数据集上进行了大量实验，与其他先进方法相比，我们的面部交换方法生成的图像具有更高的视觉质量，并且每个提出的模块都显著有效。源代码和权重将公开提供。