a b s t r a c tWe present an approach for interactively generating pen-and-ink hatching renderings based on handdrawn examples. We aim to overcome the regular and synthetic appearance of the results of existing methods by incorporating human virtuosity and illustration skills in the computer generation of such imagery. To achieve this goal, we propose to integrate an automatic style transfer with user interactions. This approach leverages the potential of example-based hatching while giving users the control and creative freedom to enhance the aesthetic appearance of the results. Using a scanned-in hatching illustration as input, we use image processing and machine learning methods to learn a model of the drawing style in the example illustration. We then apply this model to semi-automatically synthesize hatching illustrations of 3D meshes in the learned drawing style. In the learning stage, we first establish an analytical description of the hand-drawn example illustration using image processing. A 3D scene registered with the example drawing allows us to infer object-space information related to the 2D drawing elements. We employ a hierarchical style transfer model that captures drawing characteristics on four levels of abstraction, which are global, patch, stroke, and pixel levels. In the synthesis stage, an explicit representation of hatching strokes and hatching patches enables us to synthesize the learned hierarchical drawing characteristics. Our representation makes it possible to directly and intuitively interact with the hatching illustration. Amongst other interactions, users of our system can brush with patches of hatching strokes onto a 3D mesh. This interaction capability allows illustrators who are working with our system to make use of their artistic skills. Furthermore, the proposed interactions allow people without a background in hatching to interactively generate visually appealing hatching illustrations.

We present an interactive graphical approach for the explicit specification of semantics for volume visualization. This explicit and graphical specification of semantics for volumetric features allows us to visually assign meaning to both input and output parameters of the visualization mapping. This is in contrast to the implicit way of specifying semantics using transfer functions. In particular, we demonstrate how to realize a dynamic specification of semantics which allows to flexibly explore a wide range of mappings. Our approach is based on three concepts. First, we use semantic shader augmentation to automatically add rule-based rendering functionality to static visualization mappings in a shader program, while preserving the visual abstraction that the initial shader encodes. With this technique we extend recent developments that define a mapping between data attributes and visual attributes with rules, which are evaluated using fuzzy logic. Second, we let users define the semantics by analogy through brushing on renderings of the data attributes of interest. Third, the rules are specified graphically in an interface that provides visual clues for potential modifications. Together, the presented methods offer a high degree of freedom in the specification and exploration of rule-based mappings and avoid the limitations of a linguistic rule formulation.