  The research topic of sketch-to-portrait generation has witnessed a boost of
progress with deep learning techniques. The recently proposed StyleGAN
architectures achieve state-of-the-art generation ability but the original
StyleGAN is not friendly for sketch-based creation due to its unconditional
generation nature. To address this issue, we propose a direct conditioning
strategy to better preserve the spatial information under the StyleGAN
framework. Specifically, we introduce Spatially Conditioned StyleGAN
(SC-StyleGAN for short), which explicitly injects spatial constraints to the
original StyleGAN generation process. We explore two input modalities, sketches
and semantic maps, which together allow users to express desired generation
results more precisely and easily. Based on SC-StyleGAN, we present
DrawingInStyles, a novel drawing interface for non-professional users to easily
produce high-quality, photo-realistic face images with precise control, either
from scratch or editing existing ones. Qualitative and quantitative evaluations
show the superior generation ability of our method to existing and alternative
solutions. The usability and expressiveness of our system are confirmed by a
user study.
