Font Generation
21 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
Font Style that Fits an Image -- Font Generation Based on Image Context
We propose an end-to-end neural network that inputs the book cover, a target location mask, and a desired book title and outputs stylized text suitable for the cover.
Look Closer to Supervise Better: One-Shot Font Generation via Component-Based Discriminator
Automatic font generation remains a challenging research issue due to the large amounts of characters with complicated structures.
Instance Segmentation for Chinese Character Stroke Extraction, Datasets and Benchmarks
Moreover, there are no standardized benchmarks to provide a fair comparison between different stroke extraction methods, which, we believe, is a major impediment to the development of Chinese character stroke understanding and related tasks.
GAS-NeXt: Few-Shot Cross-Lingual Font Generator
Generating new fonts is a time-consuming and labor-intensive task, especially in a language with a huge amount of characters like Chinese.
Diff-Font: Diffusion Model for Robust One-Shot Font Generation
Specifically, a large stroke-wise dataset is constructed, and a stroke-wise diffusion model is proposed to preserve the structure and the completion of each generated character.
DGFont++: Robust Deformable Generative Networks for Unsupervised Font Generation
Moreover, we introduce contrastive self-supervised learning to learn a robust style representation for fonts by understanding the similarity and dissimilarities of fonts.
Neural Transformation Fields for Arbitrary-Styled Font Generation
fubinfb/ntf
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CVPR 2023
Few-shot font generation (FFG), aiming at generating font images with a few samples, is an emerging topic in recent years due to the academic and commercial values.
CF-Font: Content Fusion for Few-shot Font Generation
wangchi95/cf-font
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CVPR 2023
Content and style disentanglement is an effective way to achieve few-shot font generation.
VQ-Font: Few-Shot Font Generation with Structure-Aware Enhancement and Quantization
In this paper, we propose a VQGAN-based framework (i. e., VQ-Font) to enhance glyph fidelity through token prior refinement and structure-aware enhancement.
Few shot font generation via transferring similarity guided global style and quantization local style
To better capture the local styles, a cross-attention-based style transfer module is adopted to transfer the styles of reference glyphs to the components, where the components are self-learned discrete latent codes through vector quantization without manual definition.
