Optimized synthesis of art patterns and layered textures

Line drawings and digital arts appear everywhere, from simple icons and logos to cartoons, maps, and illustrations. We define art patterns as the subset of line drawings and digital arts that are comprised of repeated elements. There exist textures that share characteristics with art patterns. Examples of such textures include piled discrete elements with curved contours. Inspired by recent success of exemplar-based texture synthesis, in this paper, we focus on synthesizing art patterns and textures with curvilinear features from exemplars, which we cast as a global optimization problem. Our energy function for this problem measures both the appearance similarity of color patterns and shape similarity of curvilinear features between an input exemplar and a synthesized image. We develop an overall expectation-maximization-style algorithm for minimizing this energy function. The shape similarity part of the energy is minimized through an innovative application of the level set method. We further generalize our energy function and optimization algorithm to multilayer pattern and texture synthesis. Our generalized optimization can effectively handle multiple layers and synthesize valid instances of interaction.