ShapeEditer: a StyleGAN Encoder for Face Swapping
This addresses face swapping for applications like entertainment or security, but is incremental as it builds on existing StyleGAN-based approaches.
The paper tackles face swapping by proposing ShapeEditor, a two-step encoder that uses StyleGAN as backbone to integrate identity and attributes from source and target faces, achieving high-resolution, realistic results with self-supervised training. Experiments show it outperforms state-of-the-art methods in clarity and authenticity.
In this paper, we propose a novel encoder, called ShapeEditor, for high-resolution, realistic and high-fidelity face exchange. First of all, in order to ensure sufficient clarity and authenticity, our key idea is to use an advanced pretrained high-quality random face image generator, i.e. StyleGAN, as backbone. Secondly, we design ShapeEditor, a two-step encoder, to make the swapped face integrate the identity and attribute of the input faces. In the first step, we extract the identity vector of the source image and the attribute vector of the target image respectively; in the second step, we map the concatenation of identity vector and attribute vector into the $\mathcal{W+}$ potential space. In addition, for learning to map into the latent space of StyleGAN, we propose a set of self-supervised loss functions with which the training data do not need to be labeled manually. Extensive experiments on the test dataset show that the results of our method not only have a great advantage in clarity and authenticity than other state-of-the-art methods, but also reflect the sufficient integration of identity and attribute.