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.

使用逐渐增长的预训练 StyleGAN 的结构，提出一种新颖的人脸交换方法，通过分离身份和属性特征，并学习将这些特征映射到扩展潜空间，成功地实现了身份与属性的分离，并在质量和语义潜空间丰富性方面优于许多最先进的人脸交换方法，得到了广泛的实验证明。