MFIM: Megapixel Facial Identity Manipulation
Sanghyeon Na
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.