  We propose a high-quality photo-to-pencil translation method with
fine-grained control over the drawing style. This is a challenging task due to
multiple stroke types (e.g., outline and shading), structural complexity of
pencil shading (e.g., hatching), and the lack of aligned training data pairs.
To address these challenges, we develop a two-branch model that learns separate
filters for generating sketchy outlines and tonal shading from a collection of
pencil drawings. We create training data pairs by extracting clean outlines and
tonal illustrations from original pencil drawings using image filtering
techniques, and we manually label the drawing styles. In addition, our model
creates different pencil styles (e.g., line sketchiness and shading style) in a
user-controllable manner. Experimental results on different types of pencil
drawings show that the proposed algorithm performs favorably against existing
methods in terms of quality, diversity and user evaluations.
