An Infinite Parade of Giraffes: Expressive Augmentation and Complexity Layers for Cartoon Drawing
This addresses the challenge of creative image generation for designers in fields like textiles and graphics where acquiring large datasets is impractical, though it is incremental in applying existing GAN methods to a new domain.
The paper tackles the problem of generating cartoon sketches in a specific designer's style with limited data, achieving an effectively infinite number of cartoon giraffes from just nine original drawings by using expressive augmentations and chained GANs.
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.