Browsing by Author "Bermano, Amit H."

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    Learning A Stroke‐Based Representation for Fonts
    (© 2019 The Eurographics Association and John Wiley & Sons Ltd., 2019) Balashova, Elena; Bermano, Amit H.; Kim, Vladimir G.; DiVerdi, Stephen; Hertzmann, Aaron; Funkhouser, Thomas; Chen, Min and Benes, Bedrich
    Designing fonts and typefaces is a difficult process for both beginner and expert typographers. Existing workflows require the designer to create every glyph, while adhering to many loosely defined design suggestions to achieve an aesthetically appealing and coherent character set. This process can be significantly simplified by exploiting the similar structure character glyphs present across different fonts and the shared stylistic elements within the same font. To capture these correlations, we propose learning a stroke‐based font representation from a collection of existing typefaces. To enable this, we develop a stroke‐based geometric model for glyphs, a fitting procedure to reparametrize arbitrary fonts to our representation. We demonstrate the effectiveness of our model through a manifold learning technique that estimates a low‐dimensional font space. Our representation captures a wide range of everyday fonts with topological variations and naturally handles discrete and continuous variations, such as presence and absence of stylistic elements as well as slants and weights. We show that our learned representation can be used for iteratively improving fit quality, as well as exploratory style applications such as completing a font from a subset of observed glyphs, interpolating or adding and removing stylistic elements in existing fonts.
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    ToonCap: A Layered Deformable Model for Capturing Poses From Cartoon Characters
    (ACM, 2018) Fan, Xinyi; Bermano, Amit H.; Kim, Vladimir G.; Popović, Jovan; Rusinkiewicz, Szymon; Aydın, Tunç and Sýkora, Daniel
    Characters in traditional artwork such as children's books or cartoon animations are typically drawn once, in fixed poses, with little opportunity to change the characters' appearance or re-use them in a different animation. To enable these applications one can fit a consistent parametric deformable model - a puppet - to different images of a character, thus establishing consistent segmentation, dense semantic correspondence, and deformation parameters across poses. In this work we argue that a layered deformable puppet is a natural representation for hand-drawn characters, providing an effective way to deal with the articulation, expressive deformation, and occlusion that are common to this style of artwork. Our main contribution is an automatic pipeline for fitting these models to unlabeled images depicting the same character in various poses. We demonstrate that the output of our pipeline can be used directly for editing and re-targeting animations.