  With recent advances in image-to-image translation tasks, remarkable progress
has been witnessed in generating face images from sketches. However, existing
methods frequently fail to generate images with details that are semantically
and geometrically consistent with the input sketch, especially when various
decoration strokes are drawn. To address this issue, we introduce a novel W-W+
encoder architecture to take advantage of the high expressive power of W+ space
and semantic controllability of W space. We introduce an explicit intermediate
representation for sketch semantic embedding. With a semantic feature matching
loss for effective semantic supervision, our sketch embedding precisely conveys
the semantics in the input sketches to the synthesized images. Moreover, a
novel sketch semantic interpretation approach is designed to automatically
extract semantics from vectorized sketches. We conduct extensive experiments on
both synthesized sketches and hand-drawn sketches, and the results demonstrate
the superiority of our method over existing approaches on both
semantics-preserving and generalization ability.
