PattGAN: Pluralistic Facial Attribute Editing

Abstract

Facial attribute editing has witnessed substantial progress due to the increasing power of generative adversarial networks. However, existing methods mainly focus on improving the quality of facial attribute editing but ignore the diversity of facial attribute editing, which can only generate a single editing result by binary attribute labels and cannot reveal the diversity of attribute styles. To overcome this limitation, we propose a novel pluralistic facial attribute editing approach termed PattGAN (pluralistic attribute GAN). Instead of directly using the binary attribute labels to guide facial attribute editing, PattGAN first learns the disentangled representation of facial attributes by the binary attribute labels and then uses the disentangled representation of attributes to guide facial attribute editing. To this end, an independent encoder called the “attribute encoder” is introduced to extract distributions of specific attributes from face images. Furthermore, a novel swapping strategy is designed to assist the attribute encoder in modeling the disentangled representation of facial attributes by enhancing the model’s ability to learn pluralistic attributes. Coupled with the classification loss, the attribute encoder can accurately separate attribute-related information from face images. Both qualitative and quantitative experiments on the CelebA datasets show that PattGAN can achieve diverse face editing results by different exemplars. In summary, rather than other state-of-the-art methods, PattGAN performs better in diversity facial attribute editing.

Keywords

• Facial attribute editing
• generative adversarial networks
• disentangled representation
• attribute labels