We propose a framework based on Generative Adversarial Networks to
disentangle the identity and attributes of faces, such that we can conveniently
recombine different identities and attributes for identity preserving face
synthesis in open domains. Previous identity preserving face synthesis
processes are largely confined to synthesizing faces with known identities that
are already in the training dataset. To synthesize a face with identity outside
the training dataset, our framework requires one input image of that subject to
produce an identity vector, and any other input face image to extract an
attribute vector capturing, e.g., pose, emotion, illumination, and even the
background. We then recombine the identity vector and the attribute vector to
synthesize a new face of the subject with the extracted attribute. Our proposed
framework does not need to annotate the attributes of faces in any way. It is
trained with an asymmetric loss function to better preserve the identity and
stabilize the training process. It can also effectively leverage large amounts
of unlabeled training face images to further improve the fidelity of the
synthesized faces for subjects that are not presented in the labeled training
face dataset. Our experiments demonstrate the efficacy of the proposed
framework. We also present its usage in a much broader set of applications
including face frontalization, face attribute morphing, and face adversarial
example detection.

提出了一种基于生成对抗网络 (GAN) 的框架，以在开放领域中保留身份的人脸合成中，区分脸部的身份和属性。它可以方便地重新组合不同身份和属性，以合成身份保持的面部。