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.

翻译：人脸交换是一项将给定图像中的人脸身份转换为另一人的任务。本文提出了一种名为“百万像素级人脸身份操纵”（MFIM）的新型人脸交换框架。人脸交换模型需达成两个目标：首先，应能生成高质量图像。我们认为，擅长生成百万像素级图像的模型可达成此目标，但若无精心的模型设计，生成百万像素级图像通常较为困难。因此，我们的模型利用预训练的StyleGAN以GAN反演的方式有效生成百万像素图像。其次，应能有效转换给定图像的身份。具体而言，需能够主动将给定图像的身份属性（如脸型、眼睛）转换为另一人的属性，同时保留与身份无关的属性（如姿态、表情）。为实现这一目标，我们利用能够捕获多种面部属性的3DMM。具体地，通过3DMM显式监督模型生成具有期望属性的换脸图像。广泛实验表明，我们的模型达到了最先进性能。此外，我们提出了一种名为“身份混合”的新操作，通过语义混合多个人的身份来创建新身份，使用户能够自定义新的身份。