Invertible Conditional GANs for image editing
This work addresses image editing for applications like face manipulation, but it is incremental as it builds on existing cGANs by adding an encoder.
The authors tackled the problem of editing real images by inverting conditional GANs to map images into latent and conditional spaces, enabling deterministic modifications such as reconstructing and altering face images based on attributes.
Generative Adversarial Networks (GANs) have recently demonstrated to successfully approximate complex data distributions. A relevant extension of this model is conditional GANs (cGANs), where the introduction of external information allows to determine specific representations of the generated images. In this work, we evaluate encoders to inverse the mapping of a cGAN, i.e., mapping a real image into a latent space and a conditional representation. This allows, for example, to reconstruct and modify real images of faces conditioning on arbitrary attributes. Additionally, we evaluate the design of cGANs. The combination of an encoder with a cGAN, which we call Invertible cGAN (IcGAN), enables to re-generate real images with deterministic complex modifications.