  Recent deep image-to-image translation techniques allow fast generation of
face images from freehand sketches. However, existing solutions tend to overfit
to sketches, thus requiring professional sketches or even edge maps as input.
To address this issue, our key idea is to implicitly model the shape space of
plausible face images and synthesize a face image in this space to approximate
an input sketch. We take a local-to-global approach. We first learn feature
embeddings of key face components, and push corresponding parts of input
sketches towards underlying component manifolds defined by the feature vectors
of face component samples. We also propose another deep neural network to learn
the mapping from the embedded component features to realistic images with
multi-channel feature maps as intermediate results to improve the information
flow. Our method essentially uses input sketches as soft constraints and is
thus able to produce high-quality face images even from rough and/or incomplete
sketches. Our tool is easy to use even for non-artists, while still supporting
fine-grained control of shape details. Both qualitative and quantitative
evaluations show the superior generation ability of our system to existing and
alternative solutions. The usability and expressiveness of our system are
confirmed by a user study.
