  The paradigm of image-to-image translation is leveraged for the benefit of
sketch stylization via transfer of geometric textural details. Lacking the
necessary volumes of data for standard training of translation systems, we
advocate for operation at the patch level, where a handful of stylized sketches
provide ample mining potential for patches featuring basic geometric
primitives. Operating at the patch level necessitates special consideration of
full sketch translation, as individual translation of patches with no regard to
neighbors is likely to produce visible seams and artifacts at patch borders.
Aligned pairs of styled and plain primitives are combined to form input hybrids
containing styled elements around the border and plain elements within, and
given as input to a seamless translation (ST) generator, whose output patches
are expected to reconstruct the fully styled patch. An adversarial addition
promotes generalization and robustness to diverse geometries at inference time,
forming a simple and effective system for arbitrary sketch stylization, as
demonstrated upon a variety of styles and sketches.

Graphics;Computer Vision and Pattern Recognition;Machine Learning