Attribute2Font: creating fonts you want from attributes
Yizhi Wang, Yue Gao, Zhouhui Lian
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.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 1f35cbf0-2244-4834-a173-ce0e52b71d0eCited by top-tier papers15
- Understanding Design Collaboration Between Designers and Artificial Intelligence: A Systematic Literature ReviewYang Shi, Tian Gao, Xiaohan Jiao, Nan CaoCSCW 2023 · 170 citations
- TypeDance: Creating Semantic Typographic Logos from Image through Personalized GenerationShishi Xiao, Liangwei Wang, Xiaojuan Ma, Wei ZengCHI 2024 · 35 citations
- A Multi-Implicit Neural Representation for FontsPradyumna Reddy, Zhifei Zhang, Zhaowen Wang, Matthew Fisher et al.NeurIPS 2021 · 30 citations
- Aesthetic Text Logo Synthesis via Content-aware Layout InferringYizhi Wang, Guo Pu, Wenhan Luo, Yexin Wang et al.CVPR 2022 · 27 citations
- AutoStegaFont: Synthesizing Vector Fonts for Hiding Information in DocumentsXi Yang, Jie Zhang, Han Fang, Chang Liu et al.AAAI 2023 · 7 citations
Builds on3
- RelGAN: Multi-Domain Image-to-Image Translation via Relative AttributesYu-Jing Lin, Po-Wei Wu, Che-Han Chang, Edward Y. Chang et al.ICCV 2019 · 158 citations
- A Learned Representation for Scalable Vector GraphicsRaphael Gontijo Lopes, David Ha, Douglas Eck, Jonathon ShlensICCV 2019 · 153 citations
- Large-Scale Tag-Based Font Retrieval With Generative Feature LearningTianlang Chen, Zhaowen Wang, Ning Xu, Hailin Jin et al.ICCV 2019 · 33 citations
Related papers
- DA-Font: Few-Shot Font Generation via Dual-Attention Hybrid IntegrationWeiran Chen, Guiqian Zhu, Ying Li, Yi Ji et al.ACM MM 2025 · 2 citations
- FontCraft: Multimodal Font Design Using Interactive Bayesian OptimizationYuki Tatsukawa, I-Chao Shen, Mustafa Doga Dogan, Anran Qi et al.CHI 2025 · 6 citations
- FontCrafter: High-Fidelity Element-Driven Artistic Font Creation with Visual In-Context GenerationWuyang Luo, Chengkaitan Chengkaitan to Chengkai Tan, Chang Ge, Binye Hong et al.CVPR 2026 · 2 citations
- Few shot font generation via transferring similarity guided global style and quantization local styleWei Pan, Anna Zhu, Xinyu Zhou, Brian Kenji Iwana et al.ICCV 2023 · 24 citations
- VecGlypher: Unified Vector Glyph Generation with Language ModelsXiaoke Huang, Bhavul Gauri, Kam Woh Ng, Tony Ng et al.CVPR 2026 · 3 citations
