Latent Doodle Space

Abstract

We propose the concept of a latent doodle space, a low-dimensional space derived from a set of input doodles, or simple line drawings. The latent space provides a foundation for generating new drawings that are similar, but not identical to, the input examples. The two key components of this technique are 1) a heuristic algorithm for finding stroke correspondences between the drawings, and 2) the use of latent variable methods to automatically extract a low-dimensional latent doodle space from the inputs. We present two practical applications that demonstrate the utility of this idea: first, a randomized stamp tool that creates a different image on every usage; and second, personalized probabilistic fonts, a handwriting synthesis technique that mimics the idiosyncrasies of one s own handwriting.Keywords: sketch, by-example, style learning, scattered data interpolation, principal component analysis, radial basis functions, Gaussian processes, digital in-betweening, handwriting synthesis