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:
this https URL.

本文提出一种基于 GAN 模型的高分辨率人脸交换方法，通过显式地将潜在空间的语义进行分离，并通过引入基于地标的结构转换潜在方向来分离结构属性中的身份和姿态信息，进而获得丰富的生成特征。通过加入空间 - 时间约束，将这种方法进一步扩展到视频人脸交换，并在实验证明，该方法在幻觉质量和连续性方面优于现有方法。