Face swapping is a task that changes a facial identity of a given image to
that of another person. In this work, we propose a novel face-swapping
framework called Megapixel Facial Identity Manipulation (MFIM). The
face-swapping model should achieve two goals. First, it should be able to
generate a high-quality image. We argue that a model which is proficient in
generating a megapixel image can achieve this goal. However, generating a
megapixel image is generally difficult without careful model design. Therefore,
our model exploits pretrained StyleGAN in the manner of GAN-inversion to
effectively generate a megapixel image. Second, it should be able to
effectively transform the identity of a given image. Specifically, it should be
able to actively transform ID attributes (e.g., face shape and eyes) of a given
image into those of another person, while preserving ID-irrelevant attributes
(e.g., pose and expression). To achieve this goal, we exploit 3DMM that can
capture various facial attributes. Specifically, we explicitly supervise our
model to generate a face-swapped image with the desirable attributes using
3DMM. We show that our model achieves state-of-the-art performance through
extensive experiments. Furthermore, we propose a new operation called ID
mixing, which creates a new identity by semantically mixing the identities of
several people. It allows the user to customize the new identity.

本文提出了一种新颖的面部交换框架 Megapixel Facial Identity Manipulation (MFIM)，该框架能够生成高质量的图像，通过结合预训练的 StyleGAN 和 GAN-inversion 实现生成高分辨率图像。同时，通过使用 3DMM 捕捉多样的面部属性并显式监督模型生成具有所需属性的面部交换图像，实现了有效地转换给定图像的身份。在大量实验证明，我们的模型达到了最先进的性能。此外，我们还提出了一种新的操作方法叫做身份混合，可以通过语义混合多个人的身份来创建新的身份，为用户提供定制化的新身份。