  Facial image manipulation is a generation task where the output face is
shifted towards an intended target direction in terms of facial attribute and
styles. Recent works have achieved great success in various editing techniques
such as style transfer and attribute translation. However, current approaches
are either focusing on pure style transfer, or on the translation of predefined
sets of attributes with restricted interactivity. To address this issue, we
propose FacialGAN, a novel framework enabling simultaneous rich style transfers
and interactive facial attributes manipulation. While preserving the identity
of a source image, we transfer the diverse styles of a target image to the
source image. We then incorporate the geometry information of a segmentation
mask to provide a fine-grained manipulation of facial attributes. Finally, a
multi-objective learning strategy is introduced to optimize the loss of each
specific tasks. Experiments on the CelebA-HQ dataset, with CelebAMask-HQ as
semantic mask labels, show our model's capacity in producing visually
compelling results in style transfer, attribute manipulation, diversity and
face verification. For reproducibility, we provide an interactive open-source
tool to perform facial manipulations, and the Pytorch implementation of the
model.
