Latent to Latent: A Learned Mapper for Identity Preserving Editing of Multiple Face Attributes in StyleGAN-generated Images

Abstract

Several recent papers introduced techniques to adjust the attributes of human faces generated by unconditional GANs such as StyleGAN. Despite efforts to disentangle the attributes, a request to change one attribute often triggers unwanted changes to other attributes as well. More importantly, in some cases, a human observer would not recognize the edited face to belong to the same person. We propose an approach where a neural network takes as input the latent encoding of a face and the desired attribute changes and outputs the latent space encoding of the edited image. The network is trained offline using unsupervised data, with training labels generated by an off-the-shelf attribute classifier. The desired attribute changes and conservation laws, such as identity maintenance, are encoded in the training loss. The number of attributes the mapper can simultaneously modify is only limited by the attributes available to the classifier – we trained a network that handles 35 attributes, more than any previous approach. As no optimization is performed at deployment time, the computation time is negligible, allowing real-time attribute editing. Qualitative and quantitative comparisons with the current state-of-the-art show our method is better at conserving the identity of the face and restricting changes to the requested attributes.