MFIM: Megapixel Facial Identity Manipulation

• URL: http://arxiv.org/abs/2308.01536v1
• Date: Thu, 3 Aug 2023 04:36:48 GMT
• Title: MFIM: Megapixel Facial Identity Manipulation
• Authors: Sanghyeon Na
• Abstract summary: We propose a novel face-swapping framework called Megapixel Facial Identity Manipulation (MFIM) Our model exploits pretrained StyleGAN in the manner of GAN-inversion to effectively generate a megapixel image. We show that our model achieves state-of-the-art performance through extensive experiments.
• Score: 0.6091702876917281
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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