Font Generation

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

3 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

25 shown of 25 papers with code (52 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Multiple Heads are Better than One: Few-shot Font Generation with Multiple Localized Experts
  2 Apr 2021 4 repositories listed
  MX-Font extracts multiple style features not explicitly conditioned on component labels, but automatically by multiple experts to represent different local concepts, e.
• Few-shot Font Generation with Localized Style Representations and Factorization
  23 Sep 2020 3 repositories listed
  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.
• Few-shot Compositional Font Generation with Dual Memory
  21 May 2020 3 repositories listed
  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…
• Generate Like Experts: Multi-Stage Font Generation by Incorporating Font Transfer Process into Diffusion Models
  1 Jan 2024 2 repositories listed
  Based on this observation we generalize diffusion methods to model font generative process by separating the reverse diffusion process into three stages with different functions: The structure construction stage first…
• Few-Shot Font Generation by Learning Fine-Grained Local Styles
  20 May 2022 2 repositories listed
  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…
• Few-shot Font Generation with Weakly Supervised Localized Representations
  22 Dec 2021 2 repositories listed
  Existing methods learn to disentangle style and content elements by developing a universal style representation for each font style.
• DeepVecFont: Synthesizing High-quality Vector Fonts via Dual-modality Learning
  13 Oct 2021 2 repositories listed
  Automatic font generation based on deep learning has aroused a lot of interest in the last decade.
• MX-Font++: Mixture of Heterogeneous Aggregation Experts for Few-shot Font Generation
  4 Mar 2025 1 repository listed
  Few-shot Font Generation (FFG) aims to create new font libraries using limited reference glyphs, with crucial applications in digital accessibility and equity for low-resource languages, especially in multilingual…
• GRIF-DM: Generation of Rich Impression Fonts using Diffusion Models
  14 Aug 2024 1 repository listed
  In this paper, we introduce a diffusion-based method, termed \ourmethod, to generate fonts that vividly embody specific impressions, utilizing an input consisting of a single letter and a set of descriptive impression…
• FontDiffuser: One-Shot Font Generation via Denoising Diffusion with Multi-Scale Content Aggregation and Style Contrastive Learning
  19 Dec 2023 1 repository listed
  Automatic font generation is an imitation task, which aims to create a font library that mimics the style of reference images while preserving the content from source images.
• DeepCalliFont: Few-shot Chinese Calligraphy Font Synthesis by Integrating Dual-modality Generative Models
  16 Dec 2023 1 repository listed
  Few-shot font generation, especially for Chinese calligraphy fonts, is a challenging and ongoing problem.
• Few shot font generation via transferring similarity guided global style and quantization local style
  2 Sep 2023 1 repository listed Syntology ran 14 of 20 samples · 6 unverified
  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…
• VQ-Font: Few-Shot Font Generation with Structure-Aware Enhancement and Quantization
  27 Aug 2023 1 repository listed
  In this paper, we propose a VQGAN-based framework (i.
• CF-Font: Content Fusion for Few-shot Font Generation
  24 Mar 2023 1 repository listed Syntology ran 0 of 3 samples · 3 unverified · 3 pointer-only (licence)
  Content and style disentanglement is an effective way to achieve few-shot font generation.
• Neural Transformation Fields for Arbitrary-Styled Font Generation
  1 Jan 2023 1 repository listed
  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.
• DGFont++: Robust Deformable Generative Networks for Unsupervised Font Generation
  30 Dec 2022 1 repository listed
  Moreover, we introduce contrastive self-supervised learning to learn a robust style representation for fonts by understanding the similarity and dissimilarities of fonts.
• Diff-Font: Diffusion Model for Robust One-Shot Font Generation
  12 Dec 2022 1 repository listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  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.
• GAS-NeXt: Few-Shot Cross-Lingual Font Generator
  6 Dec 2022 1 repository listed
  Generating new fonts is a time-consuming and labor-intensive task, especially in a language with a huge amount of characters like Chinese.
• Instance Segmentation for Chinese Character Stroke Extraction, Datasets and Benchmarks
  25 Oct 2022 1 repository listed
  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…
• Look Closer to Supervise Better: One-Shot Font Generation via Component-Based Discriminator
  30 Apr 2022 1 repository listed Syntology ran 1 of 4 samples · 3 unverified · 4 pointer-only (licence)
  Automatic font generation remains a challenging research issue due to the large amounts of characters with complicated structures.
• Font Style that Fits an Image -- Font Generation Based on Image Context
  19 May 2021 1 repository listed
  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.
• DG-Font: Deformable Generative Networks for Unsupervised Font Generation
  7 Apr 2021 1 repository listed Syntology ran 2 of 7 samples · 5 unverified · 7 pointer-only (licence)
  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.
• StrokeGAN: Reducing Mode Collapse in Chinese Font Generation via Stroke Encoding
  16 Dec 2020 1 repository listed
  However, these deep generative models may suffer from the mode collapse issue, which significantly degrades the diversity and quality of generated results.
• GlyphGAN: Style-Consistent Font Generation Based on Generative Adversarial Networks
  29 May 2019 1 repository listed
  In GlyphGAN, the input vector for the generator network consists of two vectors: character class vector and style vector.
• Learning Typographic Style
  13 Mar 2016 1 repository listed
  Typography is a ubiquitous art form that affects our understanding, perception, and trust in what we read.

Syntology lines on 5 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.