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Attribute2Font: creating fonts you want from attributes

Published: 12 August 2020 Publication History

Abstract

Font design is now still considered as an exclusive privilege of professional designers, whose creativity is not possessed by existing software systems. Nevertheless, we also notice that most commercial font products are in fact manually designed by following specific requirements on some attributes of glyphs, such as italic, serif, cursive, width, angularity, etc. Inspired by this fact, we propose a novel model, Attribute2Font, to automatically create fonts by synthesizing visually pleasing glyph images according to user-specified attributes and their corresponding values. To the best of our knowledge, our model is the first one in the literature which is capable of generating glyph images in new font styles, instead of retrieving existing fonts, according to given values of specified font attributes. Specifically, Attribute2Font is trained to perform font style transfer between any two fonts conditioned on their attribute values. After training, our model can generate glyph images in accordance with an arbitrary set of font attribute values. Furthermore, a novel unit named Attribute Attention Module is designed to make those generated glyph images better embody the prominent font attributes. Considering that the annotations of font attribute values are extremely expensive to obtain, a semi-supervised learning scheme is also introduced to exploit a large number of unlabeled fonts. Experimental results demonstrate that our model achieves impressive performance on many tasks, such as creating glyph images in new font styles, editing existing fonts, interpolation among different fonts, etc.

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      cover image ACM Transactions on Graphics
      ACM Transactions on Graphics  Volume 39, Issue 4
      August 2020
      1732 pages
      ISSN:0730-0301
      EISSN:1557-7368
      DOI:10.1145/3386569
      Issue’s Table of Contents
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Publication History

      Published: 12 August 2020
      Published in TOG Volume 39, Issue 4

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      Author Tags

      1. deep generative models
      2. font design
      3. image synthesis
      4. style transfer
      5. type design

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      • Research-article

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      • National Natural Science Foundation of China
      • Beijing Nova Program of Science and Technology

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      • (2024)DreamFont3D: Personalized Text-to-3D Artistic Font GenerationACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657476(1-11)Online publication date: 13-Jul-2024
      • (2024)TypeDance: Creating Semantic Typographic Logos from Image through Personalized GenerationProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642185(1-18)Online publication date: 11-May-2024
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