ExtSwap: Leveraging Extended Latent Mapper for Generating High Quality
  Face Swapping
Aravinda Reddy PN K. Sreenivasa Rao Raghavendra Ramachandra Pabitra mitra
We present a novel face swapping method using the progressively growing
structure of a pre-trained StyleGAN. Previous methods use different encoder
decoder structures, embedding integration networks to produce high-quality
results, but their quality suffers from entangled representation. We
disentangle semantics by deriving identity and attribute features separately.
By learning to map the concatenated features into the extended latent space, we
leverage the state-of-the-art quality and its rich semantic extended latent
space. Extensive experiments suggest that the proposed method successfully
disentangles identity and attribute features and outperforms many
state-of-the-art face swapping methods, both qualitatively and quantitatively.