Browsing by Author "Ijiri, Takashi"

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    Fabricatable 90° Pop-ups: Interactive Transformation of a 3D Model into a Pop-up Structure
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Fujikawa, Junpei; Ijiri, Takashi; Chaine, Raphaëlle; Deng, Zhigang; Kim, Min H.
    Ninety-degree pop-ups are a type of papercraft on which a three-dimensional (3D) structure pops up when the angle of the base fold is 90°. They are fabricated by cutting and creasing a single sheet of paper. Traditional 90° pop-ups are limited to 3D shapes only comprising planar shapes because they are made of paper. In this paper, we present novel pop-ups, fabricatable 90° pop-ups that employ the 90° pop-up mechanism, consist of components with curved shapes, and can be fabricatable using a 3D printer. We propose a method for converting a 3D model into a fabricatable 90° pop-up. The user first interactively designs a layout of pop-up components, and the system automatically deforms the components using the 3D model. Because the generated pop-ups contain necessary cuts and folds, no additional assembly process is required. To demonstrate the feasibility of the proposed method, we designed and fabricated various 90° pop-ups using a 3D printer.
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    Japanese Kanji Font Style Transfer based on GAN with Unpaired Training
    (The Eurographics Association, 2018) Sakai, Hiroki; Niino, Daisuke; Ijiri, Takashi; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
    To design a whole package of Japanese font is labor consuming, since it usually contains about 30k kanji characters. To support an efficient design process, this poster attempts to adopt a style transfer algorithm for font package completion. Given two font packages where one contains all characters and the other lacks a large part, we train CycleGAN to perform style transfer between the two packages and transfer the style from the former to the latter. To illustrate the feasibility of our technique, we performed style transfer experiments and achieved visually plausible results by using a relatively small training data set.