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|a 10.1109/TVCG.2022.3183400
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|a eng
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| 100 |
1 |
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|a Liu, Ying-Tian
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Learning Implicit Glyph Shape Representation
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|c 2023
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|a Date Revised 04.09.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Automatic generation of fonts can greatly facilitate the font design process, and provide prototypes where designers can draw inspiration from. Existing generation methods are mainly built upon rasterized glyph images to utilize the successful convolutional architecture, but ignore the vector nature of glyph shapes. We present an implicit representation, modeling each glyph as shape primitives enclosed by several quadratic curves. This structured implicit representation is shown to be better suited for glyph modeling, and enables rendering glyph images at arbitrary high resolutions. Our representation gives high-quality glyph reconstruction and interpolation results, and performs well on the challenging one-shot font style transfer task comparing to other alternatives both qualitatively and quantitatively
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| 650 |
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|a Journal Article
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| 700 |
1 |
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|a Guo, Yuan-Chen
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Yi-Xiao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Chen
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Song-Hai
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on visualization and computer graphics
|d 1996
|g 29(2023), 10 vom: 16. Okt., Seite 4172-4182
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|g volume:29
|g year:2023
|g number:10
|g day:16
|g month:10
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|u http://dx.doi.org/10.1109/TVCG.2022.3183400
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