Conditional CycleGAN for Attribute Guided Face Image Generation

State-of-the-art techniques in Generative Adversarial Networks (GANs) such as cycleGAN is able to learn the mapping of one image domain X to another image domain Y using unpaired image data. We extend the cycleGAN to Conditional cycleGAN such that the mapping from X to Y is subjected to attribute condition Z. Using face image generation as an application example, where X is a low resolution face image, Y is a high resolution face image, and Z is a set of attributes related to facial appearance (e.g. gender, hair color, smile), we present our method to incorporate Z into the network, such that the hallucinated high resolution face image Y ′ not only satisfies the low resolution constrain inherent in X , but also the attribute condition prescribed by Z. Using face feature vector extracted from face verification network as Z, we demonstrate the efficacy of our approach on identitypreserving face image super-resolution. Our approach is general and applicable to high-quality face image generation where specific facial attributes can be controlled easily in the automatically generated results.