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 highres 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 high-quality 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 high-quality and interesting results on identity transfer. We demonstrate three applications on identityguided conditional CycleGAN: identity-preserving face superresolution,
face swapping, and frontal face generation, which consistently show the
advantage of our new method