Font Generation

17 papers with code • 1 benchmarks • 3 datasets

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Use these libraries to find Font Generation models and implementations

Most implemented papers

Multiple Heads are Better than One: Few-shot Font Generation with Multiple Localized Experts

clovaai/mxfont ICCV 2021

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

clovaai/dmfont ECCV 2020

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 23 Sep 2020

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

yizhiwang96/deepvecfont 13 Oct 2021

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 22 Dec 2021

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

tlc121/FsFont CVPR 2022

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

kaonashi-tyc/Rewrite 13 Mar 2016

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

joshpc/StyledFontGAN 29 May 2019

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

JinshanZeng/StrokeGAN 16 Dec 2020

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 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.