The great advancements of generative adversarial networks and face
recognition models in computer vision have made it possible to swap identities
on images from single sources. Although a lot of studies seems to have proposed
almost satisfactory solutions, we notice previous methods still suffer from an
identity-attribute entanglement that causes undesired attributes swapping
because widely used identity encoders, eg, ArcFace, have some crucial attribute
biases owing to their pretraining on face recognition tasks. To address this
issue, we design BlendFace, a novel identity encoder for face-swapping. The key
idea behind BlendFace is training face recognition models on blended images
whose attributes are replaced with those of another mitigates inter-personal
biases such as hairsyles. BlendFace feeds disentangled identity features into
generators and guides generators properly as an identity loss function.
Extensive experiments demonstrate that BlendFace improves the
identity-attribute disentanglement in face-swapping models, maintaining a
comparable quantitative performance to previous methods.

本研究提出了 BlendFace，一种用于换脸的新型身份编码器，通过在混合图像上训练面部识别模型并引导生成器，解决了现有方法中存在的身份 - 属性纠缠问题。大量实验证明 BlendFace 在换脸模型中提高了身份与属性的分离效果，并且在定量性能上与之前的方法相当。