Creating high-quality label layouts in a particular visual style is a time-consuming process. Although automated labeling algorithms can aid the layout process, expert design knowledge is required to tune these algorithms so that they produce layouts which meet the designer's expectations. We propose a system which can learn a label layout style from a single example layout and then apply this style to new labeling problems. Because designers find it much easier to create example layouts than tune algorithmic parameters, our system provides a more natural workflow for graphic designers. We demonstrate that our system is capable of learning a variety of label layout styles from examples.

This paper presents an approach for determining stroke thickness in computer-generated illustrations of smooth surfaces. We assume that dark strokes are drawn to approximate the dark regions of the shaded surface. This assumption leads to a simple formula for thickness of contours and suggestive contours; this formula depends on depth, radial curvature, and light direction in a manner that reproduces aspects of thickness observed in hand-made drawings. These strokes convey local shape and depth relationships, and produce appealing imagery. Our method is simple to implement, provides temporally-coherent strokes, and runs at interactive rates.