# DeepCalliFont: Few-shot Chinese Calligraphy Font Synthesis by Integrating Dual-modality Generative Models

arXiv: https://arxiv.org/abs/2312.10314 · HTML: https://mlsanity.com/paper/2312.10314/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 39 references that are in the map.
- First posted on arXiv 16 Dec 2023.

## Builds on (most cited first, 25 of 25)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation](https://mlsanity.com/paper/2007.11301/) (2020)
- [Im2Vec: Synthesizing Vector Graphics without Vector Supervision](https://mlsanity.com/paper/2102.02798/) (2021)
- [Multi-Content GAN for Few-Shot Font Style Transfer](https://mlsanity.com/paper/1712.00516/) (2017)
- [A Learned Representation for Scalable Vector Graphics](https://mlsanity.com/paper/1904.02632/) (2019)
- [DG-Font: Deformable Generative Networks for Unsupervised Font Generation](https://mlsanity.com/paper/2104.03064/) (2021)
- [Few-shot Font Generation with Localized Style Representations and Factorization](https://mlsanity.com/paper/2009.11042/) (2020)
- [Multiple Heads are Better than One: Few-shot Font Generation with Multiple Localized Experts](https://mlsanity.com/paper/2104.00887/) (2021)
- [Few-Shot Font Generation by Learning Fine-Grained Local Styles](https://mlsanity.com/paper/2205.09965/) (2022)
- [Look Closer to Supervise Better: One-Shot Font Generation via Component-Based Discriminator](https://mlsanity.com/paper/2205.00146/) (2022)
- [Deep Parametric Shape Predictions using Distance Fields](https://mlsanity.com/paper/1904.08921/) (2019)
- [Learning to Write Stylized Chinese Characters by Reading a Handful of Examples](https://mlsanity.com/paper/1712.06424/) (2017)
- [CalliGAN: Style and Structure-aware Chinese Calligraphy Character Generator](https://mlsanity.com/paper/2005.12500/) (2020)
- [DeepVecFont-v2: Exploiting Transformers to Synthesize Vector Fonts with Higher Quality](https://mlsanity.com/paper/2303.14585/) (2023)
- [Drawing and Recognizing Chinese Characters with Recurrent Neural Network](https://mlsanity.com/paper/1606.06539/) (2016)
- [ZiGAN: Fine-grained Chinese Calligraphy Font Generation via a Few-shot Style Transfer Approach](https://mlsanity.com/paper/2108.03596/) (2021)
- [XMP-Font: Self-Supervised Cross-Modality Pre-training for Few-Shot Font Generation](https://mlsanity.com/paper/2204.05084/) (2022)
- [SE-GAN: Skeleton Enhanced GAN-based Model for Brush Handwriting Font Generation](https://mlsanity.com/paper/2204.10484/) (2022)
- [Automatic Generation of Typographic Font from a Small Font Subset](https://mlsanity.com/paper/1701.05703/) (2017)
- [Few-Shot Font Generation with Deep Metric Learning](https://mlsanity.com/paper/2011.02206/) (2020)

## Built on by (most cited first, 4 of 4)

- [UniCalli: A Unified Diffusion Framework for Column-Level Generation and Recognition of Chinese Calligraphy](https://mlsanity.com/paper/2510.13745/) (2025)
- [Computer-Aided Multi-Stroke Character Simplification by Stroke Removal](https://mlsanity.com/paper/2506.23106/) (2025)
- [DNA: Dual-branch Network with Adaptation for Open-Set Online Handwriting Generation](https://mlsanity.com/paper/2511.22064/) (2025)
- [InkDiffuser: High-Fidelity One-shot Chinese Calligraphy via Differentiable Morphological Optimization](https://mlsanity.com/paper/2605.05865/) (2026)

## Abstract (arXiv)

Few-shot font generation, especially for Chinese calligraphy fonts, is a challenging and ongoing problem. With the help of prior knowledge that is mainly based on glyph consistency assumptions, some recently proposed methods can synthesize high-quality Chinese glyph images. However, glyphs in calligraphy font styles often do not meet these assumptions. To address this problem, we propose a novel model, DeepCalliFont, for few-shot Chinese calligraphy font synthesis by integrating dual-modality generative models. Specifically, the proposed model consists of image synthesis and sequence generation branches, generating consistent results via a dual-modality representation learning strategy. The two modalities (i.e., glyph images and writing sequences) are properly integrated using a feature recombination module and a rasterization loss function. Furthermore, a new pre-training strategy is adopted to improve the performance by exploiting large amounts of uni-modality data. Both qualitative and quantitative experiments have been conducted to demonstrate the superiority of our method to other state-of-the-art approaches in the task of few-shot Chinese calligraphy font synthesis. The source code can be found at https://github.com/lsflyt-pku/DeepCalliFont.