NPAR13

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https://diglib.eg.org/handle/10.2312/992

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    Towards effective evaluation of geometric texture synthesis algorithms
    (ACM, 2013) AlMeraj, Zainab; Kaplan, Craig S.; Asente, Paul; Forrester Cole and Cindy Grimm
    In recent years, an increasing number of example-based Geometric Texture Synthesis (GTS) algorithms have been proposed. However, there have been few attempts to evaluate these algorithms rigorously. We are driven by this lack of validation and the simplicity of the GTS problem to look closer at perceptual similarity between geometric arrangements. Using samples from a geological database, our research first establishes a dataset of geometric arrangements gathered from multiple synthesis sources. We then employ the dataset in two evaluation studies. Collectively these empirical methods provide formal foundations for perceptual studies in GTS, insight into the robustness of GTS algorithms and a better understanding of similarity in the context of geometric texture arrangements.
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    Preface and Table of Contents
    (ACM, 2013) Forrester Cole and Cindy Grimm
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    ELASTIFACE: Matching and Blending Textured Faces
    (ACM, 2013) Zell, Eduard; Botsch, Mario; Forrester Cole and Cindy Grimm
    In this paper we present ELASTIFACE, a simple and versatile method for establishing correspondence between textured face models, either for the construction of a blend-shape facial rig or for the exploration of new characters by morphing between a set of input models. While there exists a wide variety of approaches for inter-surface mapping and mesh morphing, most techniques are not suitable for our application: They either require the insertion of additional vertices, are limited to topological planes or spheres, are restricted to near-isometric input meshes, and/or are algorithmically and computationally involved. In contrast, our method extends linear non-rigid registration techniques to allow for strongly varying input geometries. It is geometrically intuitive, simple to implement, computationally efficient, and robustly handles highly non-isometric input models. In order to match the requirements of other applications, such as recent perception studies, we further extend our geometric matching to the matching of input textures and morphing of geometries and rendering styles.
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    Rasterizing and antialiasing vector line art in the pixel art style
    (ACM, 2013) Inglis, Tiffany C.; Vogel, Daniel; Kaplan, Craig S.; Forrester Cole and Cindy Grimm
    Pixel artists rasterize vector shapes by hand to minimize artifacts at low resolutions and emphasize the aesthetics of visible pixels. We describe Superpixelator, an algorithm that automates this process by rasterizing vector line art at a low resolution pixel art style. Our technique successfully eliminates most rasterization artifacts and draws smoother curves. To draw shapes more effectively, we use optimization techniques to preserve shape properties such as symmetry, aspect ratio, and sharp angles. Our algorithm also supports ''manual antialiasing, the style of antialiasing used in pixel art. Professional pixel artists report that Superpixelator's results are as good, or better, than hand-rasterized drawings by artists.