Fast Inverse Mapping of Face GANs

Abstract

Generative adversarial networks (GANs) synthesize realistic images from random latent vectors. While many studies have explored various training configurations and architectures for GANs, the problem of inverting the generator of GANs has been inadequately investigated. We train a ResNet architecture to map given faces to latent vectors that can be used to generate faces nearly identical to the target. We use a perceptual loss to embed face details in the recovered latent vector while maintaining visual quality using a pixel loss. The vast majority of studies on latent vector recovery are very slow and perform well only on generated images. We argue that our method can be used to determine a fast mapping between real human faces and latent-space vectors that contain most of the important face style details. At last, we demonstrate the performance of our approach on both real and generated faces.

Identity-Guided Face Generation with Multi-Modal Contour Conditions

“…The former maps a randomly sampled noise to a style latent code of 512 dimensions while the latter produces satisfying images with this latent code and a constant input by Adaptive Instance Normalization layers. To deal with conditional synthesis tasks, recent methods [5,8,9,10,11,12] use a technique called GAN inversion [13]. GAN inversion is to map an image into the latent space of a pretrained GAN model for a desired latent code, which can be faithfully reconstructed afterwards.…”