Unsupervised statistical sketching for non-photorealistic rendering models

This paper investigates the use of the Bayesian inference for devising an unsupervised sketch rendering procedure. As likelihood model of this inference, we exploit the recent statistical model of the gradient vector field distribution proposed by Destrempes et al. for contour detection. A global prior deformation model for each pencil stroke is also considered. In this Bayesian framework, the placement of each stroke is viewed as the search of the Maximum A Posteriori estimation of the posterior distribution of its deformations. We use a stochastic optimization algorithm in order to find these optimal deformations. This yields an unsupervised method to create realistic hand-sketched pencil drawings. Combined with an example-based local rendering model, used to transfer the textural tone value of a given depiction style, the proposed scheme allows to simulate automatic synthesis of various artistic illustration styles.