Im2Pencil: Controllable Pencil Illustration from Photographs
This work addresses the challenge of creating controllable pencil illustrations for artists and designers, but it is incremental as it builds on existing photo-to-sketch translation methods.
The paper tackles the problem of generating high-quality pencil illustrations from photographs with fine-grained control over drawing styles, achieving favorable performance against existing methods in quality, diversity, and user evaluations.
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.