High Fidelity Face-Swapping With Style ConvTransformer and Latent Space Selection

Face-swapping technology has been widely used in people's life, and people also put forward higher requirements for it. Most of the current face-swapping methods are difficult to generate a high-definition face image. Through StyleGAN, we can generate high-definition face images. However, face-swapping with StyleGAN is still challenging. Firstly, we need to map the target image to the latent space of StyleGAN. Many tasks need to map the input image to a new latent space for face-swapping, because identity features are complex and challenging to map to specific latent space layers directly. So face-swapping is completed in the remapping process, which consumes excess computing resources for reconstruction. And the generated image is difficult to maintain the original image color, face attributes, background and other attributes. We propose a new method, which only edits the code of w+ latent space of StyleGAN to complete the face-swapping and generate high-definition face images. We propose the GAN inversion method to improve the effect of face swapping, which combines convolution networks' advantages in extracting texture features and the benefits of transformers in extracting structure features. In the latent space of StyleGAN, the low-level feature layer is dominated by structure information, and the high-level feature layer is overwhelmed by texture information. Furthermore, we propose latent space selection, through which the neural network can learn disentangled representations of identity information in the latent space. Finally, we improved the post-processing process of face swapping to keep the image's background. Our method can complete face-swapping by editing the w+ space. Thus, high-quality face image can be generated and a lot of computing resource is saved on image reconstruction. At the same time, our method can keep other attributes better in the face-swapping process.