Taking full advantage of the excellent performance of StyleGAN, style transfer-based face swapping methods have been extensively investigated recently. However, these studies require separate face segmentation and blending modules for successful face swapping, and the fixed selection of the manipulated latent code in these works is reckless, thus degrading face swapping quality, generalizability, and practicability. This paper proposes a novel and end-to-end integrated framework for high resolution and attribute preservation face swapping via Adaptive Latent Representation Learning. Specifically, we first design a multi-task dual-space face encoder by sharing the underlying feature extraction network to simultaneously complete the facial region perception and face encoding. This encoder enables us to control the face pose and attribute individually, thus enhancing the face swapping quality. Next, we propose an adaptive latent codes swapping module to adaptively learn the mapping between the facial attributes and the latent codes and select effective latent codes for improved retention of facial attributes. Finally, the initial face swapping image generated by StyleGAN2 is blended with the facial region mask generated by our encoder to address the background blur problem. Our framework integrating facial perceiving and blending into the end-to-end training and testing process can achieve high realistic face-swapping on wild faces without segmentation masks. Experimental results demonstrate the superior performance of our approach over state-of-the-art methods.

翻译：近年来，基于风格迁移的换脸方法充分利用了StyleGAN卓越的性能，得到了广泛研究。然而，这些方法需要单独的面部分割和融合模块才能实现成功的换脸，且现有工作中对操纵隐式编码的固定选择较为鲁莽，从而降低了换脸质量、泛化能力和实用性。本文提出了一种新颖的端到端集成框架，通过自适应隐式表征学习实现高分辨率且保留属性的换脸。具体而言，我们首先设计了一个多任务双空间人脸编码器，通过共享底层特征提取网络来同时完成面部区域感知和人脸编码。该编码器使我们能够分别控制人脸姿态和属性，从而提升换脸质量。其次，我们提出了一种自适应隐式编码交换模块，能够自适应地学习面部属性与隐式编码之间的映射关系，并选择有效的隐式编码以更好地保留面部属性。最后，将StyleGAN2生成的初始换脸图像与编码器生成的面部区域掩膜进行融合，以解决背景模糊问题。我们的框架将面部感知与融合集成到端到端的训练和测试过程中，无需分割掩膜即可在野生人脸图像上实现高度逼真的换脸。实验结果表明，我们的方法在性能上优于当前最先进的方法。