  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.
