Attribute2Font: creating fonts you want from attributes
Yizhi Wang, Yue Gao, Zhouhui Lian
摘要
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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引用它的顶会 Paper15
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- TypeDance: Creating Semantic Typographic Logos from Image through Personalized GenerationShishi Xiao, Liangwei Wang, Xiaojuan Ma, Wei ZengCHI 2024 · 被引用 35 次
- A Multi-Implicit Neural Representation for FontsPradyumna Reddy, Zhifei Zhang, Zhaowen Wang, Matthew Fisher 等NeurIPS 2021 · 被引用 30 次
- Aesthetic Text Logo Synthesis via Content-aware Layout InferringYizhi Wang, Guo Pu, Wenhan Luo, Yexin Wang 等CVPR 2022 · 被引用 27 次
- AutoStegaFont: Synthesizing Vector Fonts for Hiding Information in DocumentsXi Yang, Jie Zhang, Han Fang, Chang Liu 等AAAI 2023 · 被引用 7 次
它引用的顶会 Paper3
- RelGAN: Multi-Domain Image-to-Image Translation via Relative AttributesYu-Jing Lin, Po-Wei Wu, Che-Han Chang, Edward Y. Chang 等ICCV 2019 · 被引用 158 次
- A Learned Representation for Scalable Vector GraphicsRaphael Gontijo Lopes, David Ha, Douglas Eck, Jonathon ShlensICCV 2019 · 被引用 153 次
- Large-Scale Tag-Based Font Retrieval With Generative Feature LearningTianlang Chen, Zhaowen Wang, Ning Xu, Hailin Jin 等ICCV 2019 · 被引用 33 次
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