  Interactive facial image manipulation attempts to edit single and multiple
face attributes using a photo-realistic face and/or semantic mask as input. In
the absence of the photo-realistic image (only sketch/mask available), previous
methods only retrieve the original face but ignore the potential of aiding
model controllability and diversity in the translation process. This paper
proposes a sketch-to-image generation framework called S2FGAN, aiming to
improve users' ability to interpret and flexibility of face attribute editing
from a simple sketch. The proposed framework modifies the constrained latent
space semantics trained on Generative Adversarial Networks (GANs). We employ
two latent spaces to control the face appearance and adjust the desired
attributes of the generated face. Instead of constraining the translation
process by using a reference image, the users can command the model to retouch
the generated images by involving the semantic information in the generation
process. In this way, our method can manipulate single or multiple face
attributes by only specifying attributes to be changed. Extensive experimental
results on CelebAMask-HQ dataset empirically shows our superior performance and
effectiveness on this task. Our method successfully outperforms
state-of-the-art methods on attribute manipulation by exploiting greater
control of attribute intensity.
