  In this paper, we explore creative image generation constrained by small
data. To partially automate the creation of cartoon sketches consistent with a
specific designer's style, where acquiring a very large original image set is
impossible or cost prohibitive, we exploit domain specific knowledge for a huge
reduction in original image requirements, creating an effectively infinite
number of cartoon giraffes from just nine original drawings. We introduce
"expressive augmentations" for cartoon sketches, mathematical transformations
that create broad domain appropriate variation, far beyond the usual affine
transformations, and we show that chained GANs models trained on the temporal
stages of drawing or "complexity layers" can effectively add character
appropriate details and finish new drawings in the designer's style.
  We discuss the application of these tools in design processes for textiles,
graphics, architectural elements and interior design.

Graphics;Computer Vision and Pattern Recognition;Machine Learning;Machine Learning