Attribute-Guided Face Generation Using Conditional CycleGAN

TL;DR

This paper introduces a conditional CycleGAN for attribute-guided face generation that produces high-resolution, attribute-controlled, and identity-preserving face images from low-resolution inputs, even with unpaired training data.

Contribution

It proposes a novel conditional CycleGAN framework capable of unpaired training and attribute control for face generation, extending to identity transfer tasks.

Findings

Produces realistic, attribute-controlled face images

Enables identity transfer with face verification integration

Demonstrates applications in superresolution, face swapping, and frontal face synthesis

Abstract

We are interested in attribute-guided face generation: given a low-res face input image, an attribute vector that can be extracted from a high-res image (attribute image), our new method generates a high-res face image for the low-res input that satisfies the given attributes. To address this problem, we condition the CycleGAN and propose conditional CycleGAN, which is designed to 1) handle unpaired training data because the training low/high-res and high-res attribute images may not necessarily align with each other, and to 2) allow easy control of the appearance of the generated face via the input attributes. We demonstrate impressive results on the attribute-guided conditional CycleGAN, which can synthesize realistic face images with appearance easily controlled by user-supplied attributes (e.g., gender, makeup, hair color, eyeglasses). Using the attribute image as identity to produce the corresponding conditional vector and by incorporating a face verification network, the attribute-guided network becomes the identity-guided conditional CycleGAN which produces impressive and interesting results on identity transfer. We demonstrate three applications on identity-guided conditional CycleGAN: identity-preserving face superresolution, face swapping, and frontal face generation, which consistently show the advantage of our new method.