GPSwap: High-resolution face swapping based on StyleGAN prior

Abstract

Existing high-resolution face-swapping works are still challenges in preserving identity consistency while maintaining high visual quality. We present a novel high-resolution face-swapping method GPSwap, which is based on StyleGAN prior. To better preserves identity consistency, the proposed facial feature recombination network fully leverages the properties of both w space and encoders to decouple identities. Furthermore, we presents the image reconstruction module aligns and blends images in FS space, which further supplements facial details and achieves natural blending. It not only improves image resolution but also optimizes visual quality. Extensive experiments and user studies demonstrate that GPSwap is superior to state-of-the-art high-resolution face-swapping methods in terms of image quality and identity consistency. In addition, GPSwap saves nearly 80% of training costs compared to other high-resolution face-swapping works.