Learning Implicit Glyph Shape Representation
| Autor: | Ying-Tian Liu, Yuan-Chen Guo, Yi-Xiao Li, Chen Wang, Song-Hai Zhang |
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| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Computer Vision and Pattern Recognition (cs.CV) Signal Processing ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Computer Science - Computer Vision and Pattern Recognition Computer Vision and Pattern Recognition Computer Graphics and Computer-Aided Design Software ComputingMethodologies_COMPUTERGRAPHICS |
| Zdroj: | IEEE Transactions on Visualization and Computer Graphics. :1-12 |
| ISSN: | 2160-9306 1077-2626 |
| DOI: | 10.1109/tvcg.2022.3183400 |
| Popis: | In this paper, we present a novel implicit glyph shape representation, which models glyphs as shape primitives enclosed by quadratic curves, and naturally enables generating glyph images at arbitrary high resolutions. Experiments on font reconstruction and interpolation tasks verified that this structured implicit representation is suitable for describing both structure and style features of glyphs. Furthermore, based on the proposed representation, we design a simple yet effective disentangled network for the challenging one-shot font style transfer problem, and achieve the best results comparing to state-of-the-art alternatives in both quantitative and qualitative comparisons. Benefit from this representation, our generated glyphs have the potential to be converted to vector fonts through post-processing, reducing the gap between rasterized images and vector graphics. We hope this work can provide a powerful tool for 2D shape analysis and synthesis, and inspire further exploitation in implicit representations for 2D shape modeling. |
| Databáze: | OpenAIRE |
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