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 ${\it Conditional}$ 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 identity-preserving 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.