  We present a novel high-resolution face swapping method using the inherent
prior knowledge of a pre-trained GAN model. Although previous research can
leverage generative priors to produce high-resolution results, their quality
can suffer from the entangled semantics of the latent space. We explicitly
disentangle the latent semantics by utilizing the progressive nature of the
generator, deriving structure attributes from the shallow layers and appearance
attributes from the deeper ones. Identity and pose information within the
structure attributes are further separated by introducing a landmark-driven
structure transfer latent direction. The disentangled latent code produces rich
generative features that incorporate feature blending to produce a plausible
swapping result. We further extend our method to video face swapping by
enforcing two spatio-temporal constraints on the latent space and the image
space. Extensive experiments demonstrate that the proposed method outperforms
state-of-the-art image/video face swapping methods in terms of hallucination
quality and consistency. Code can be found at:
https://github.com/cnnlstm/FSLSD_HiRes.
