Attribute2Font: Creating Fonts You Want From Attributes
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Abstract (arXiv)
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.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 May 2020.
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Citing papers per year: 2021: 5; 2022: 2; 2023: 3; 2024: 2.
12 papers.
- 2021High-Fidelity and Arbitrary Face Editingcited by 35
- 2023DeepVecFont-v2: Exploiting Transformers to Synthesize Vector Fonts with Higher Qualitycited by 27
- 2021A Multi-Implicit Neural Representation for Fontscited by 21
- 2022Aesthetic Text Logo Synthesis via Content-aware Layout Inferringcited by 19
- 2023DualVector: Unsupervised Vector Font Synthesis with Dual-Part Representationcited by 16
- 2021DeepVecFont: Synthesizing High-quality Vector Fonts via Dual-modality Learningcited by 14
- 2021Impressions2Font: Generating Fonts by Specifying Impressionscited by 10
- 2024TypeDance: Creating Semantic Typographic Logos from Image through Personalized Generationcited by 10
- 2024FontCLIP: A Semantic Typography Visual-Language Model for Multilingual Font Applicationscited by 8
- 2021Which Parts Determine the Impression of the Font?cited by 7
- 2023VecFontSDF: Learning to Reconstruct and Synthesize High-quality Vector Fonts via Signed Distance Functionscited by 7
- 2022Font Generation with Missing Impression Labelscited by 6
- 2024FontCLIP: A Semantic Typography Visual-Language Model for Multilingual Font Applications11 Mar 2024
- 2024TypeDance: Creating Semantic Typographic Logos from Image through Personalized Generation20 Jan 2024
- 2023DualVector: Unsupervised Vector Font Synthesis with Dual-Part Representation17 May 2023
- 2023DeepVecFont-v2: Exploiting Transformers to Synthesize Vector Fonts with Higher Quality25 Mar 2023
- 2023VecFontSDF: Learning to Reconstruct and Synthesize High-quality Vector Fonts via Signed Distance Functions22 Mar 2023
- 2022Aesthetic Text Logo Synthesis via Content-aware Layout Inferring6 Apr 2022
- 2022Font Generation with Missing Impression Labels19 Mar 2022
- 2021DeepVecFont: Synthesizing High-quality Vector Fonts via Dual-modality Learning13 Oct 2021
- 2021A Multi-Implicit Neural Representation for Fonts12 Jun 2021
- 2021High-Fidelity and Arbitrary Face Editing29 Mar 2021
- 2021Which Parts Determine the Impression of the Font?26 Mar 2021
- 2021Impressions2Font: Generating Fonts by Specifying Impressions18 Mar 2021