Font Generation
17 papers with code • 1 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Font Generation
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Libraries
Use these libraries to find Font Generation models and implementations
Book Cover Dataset
CCSE
Dafonts Free
Most implemented papers
Multiple Heads are Better than One: Few-shot Font Generation with Multiple Localized Experts
MX-Font extracts multiple style features not explicitly conditioned on component labels, but automatically by multiple experts to represent different local concepts, e. g., left-side sub-glyph.
Few-shot Compositional Font Generation with Dual Memory
By utilizing the compositionality of compositional scripts, we propose a novel font generation framework, named Dual Memory-augmented Font Generation Network (DM-Font), which enables us to generate a high-quality font library with only a few samples.
Few-shot Font Generation with Localized Style Representations and Factorization
clovaai/lffont
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However, learning component-wise styles solely from reference glyphs is infeasible in the few-shot font generation scenario, when a target script has a large number of components, e. g., over 200 for Chinese.
DeepVecFont: Synthesizing High-quality Vector Fonts via Dual-modality Learning
Automatic font generation based on deep learning has aroused a lot of interest in the last decade.
Few-shot Font Generation with Weakly Supervised Localized Representations
clovaai/fewshot-font-generation
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Existing methods learn to disentangle style and content elements by developing a universal style representation for each font style.
Few-Shot Font Generation by Learning Fine-Grained Local Styles
Instead of explicitly disentangling global or component-wise modeling, the cross-attention mechanism can attend to the right local styles in the reference glyphs and aggregate the reference styles into a fine-grained style representation for the given content glyphs.
Learning Typographic Style
Typography is a ubiquitous art form that affects our understanding, perception, and trust in what we read.
GlyphGAN: Style-Consistent Font Generation Based on Generative Adversarial Networks
In GlyphGAN, the input vector for the generator network consists of two vectors: character class vector and style vector.
StrokeGAN: Reducing Mode Collapse in Chinese Font Generation via Stroke Encoding
However, these deep generative models may suffer from the mode collapse issue, which significantly degrades the diversity and quality of generated results.
DG-Font: Deformable Generative Networks for Unsupervised Font Generation
ecnuycxie/DG-Font
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CVPR 2021
Font generation is a challenging problem especially for some writing systems that consist of a large number of characters and has attracted a lot of attention in recent years.
