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.

翻译：摘要：我们提出了一种新颖的人脸交换方法，该方法利用预训练StyleGAN的渐进式增长结构。先前的方法采用不同的编码器-解码器结构及嵌入集成网络来生成高质量结果，但其质量受限于纠缠表示。我们通过分别提取身份特征与属性特征来解耦语义。通过将拼接后的特征映射到扩展潜在空间中，我们利用了该空间的最先进质量及其丰富的语义扩展潜在表示。大量实验表明，所提出的方法成功解耦了身份与属性特征，并在定性与定量上均优于多种最先进的人脸交换方法。