IShengFang / TypographyResearchCollection

The research collection of typography

Home Page:https://ishengfang.github.io/TypographyResearchCollection/

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Typography Research Collection

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Typography is the cross between technology and liberal arts. This page is a research collection that includes computer graphics, computer vision, machine learning that related to typography.

Visual Text Generation and Editing

  • Generate Like Experts: Multi-Stage Font Generation by Incorporating Font Transfer Process into Diffusion Models

    • Bin Fu, Fanghua Yu, Anran Liu, Zixuan Wang, Jie Wen, Junjun He, Yu Qiao
    • CVPR 2024
  • VecFusion: Vector Font Generation with Diffusion

    • Vikas Thamizharasan, Difan Liu, Shantanu Agarwal, Matthew Fisher, Michael Gharbi, Oliver Wang, Alec Jacobson, Evangelos Kalogerakis
    • CVPR 2024
    • [arxiv]

  • FontDiffuser: One-Shot Font Generation via Denoising Diffusion with Multi-Scale Content Aggregation and Style Contrastive Learning

  • AnyText: Multilingual Visual Text Generation And Editing
    • Yuxiang Tuo, Wangmeng Xiang, Jun-Yan He, Yifeng Geng∗, Xuansong Xie
    • ICLR 2024
    • [paper][code][demo]

  • GlyphControl: Glyph Conditional Control for Visual Text Generation
    • Yukang Yang, Dongnan Gui, Yuhui Yuan, Weicong Liang, Haisong Ding, Han Hu, Kai Chen
    • NeurIPS 2023
    • [paper][code]

  • DiffUTE: Universal Text Editing Diffusion Model
    • Haoxing Chen, Zhuoer Xu1, Zhangxuan Gu*, Jun Lan, Xing Zheng, Yaohui Li, Changhua Meng, Huijia Zhu, Weiqiang Wang
      • ∗Corresponding author
    • NeurIPS 2023
    • [paper][code]

  • GlyphDraw: Learning to Draw Chinese Characters in Image Synthesis Models Coherently

  • DeepFloyd IF
    • Inspired by "Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding", NeurIPS2022
      • aka Imagen
    • [code]

  • Character-Aware Models Improve Visual Text Rendering
    • arxiv
    • Rosanne Liu∗, Dan Garrette∗, Chitwan Saharia, William Chan, Adam Roberts, Sharan Narang, Irina Blok, RJ Mical, Mohammad Norouzi, Noah Constant∗
      • *Equal contribution
    • [paper]

  • OCR-VQGAN: Taming Text-within-Image Generation

Font Stye Transfer and Glyph Generation

NN Approach

  • DS-Fusion: Artistic Typography via Discriminated and Stylized Diffusion

  • Word-As-Image for Semantic Typography
    • Shir Iluz*, Yael Vinker*, Amir Hertz, Daniel Berio, Daniel Cohen-Or, Ariel Shamir
      • *Denotes equal contribution
    • SIGGRAPH 2023 - Honorable Mention Award
    • [project page][paper][code][demo]

  • DeepVecFont-v2: Exploiting Transformers to Synthesize Vector Fonts with Higher Quality
    • Yuqing Wang, Yizhi Wang, Longhui Yu, Yuesheng Zhu, Zhouhui Lian
    • CVPR 2023
    • [paper(CVPR)][code]

  • DeepVecFont: Synthesizing High-quality Vector Fonts via Dual-modality Learning

  • StrokeStyles: Stroke-based Segmentation and Stylization of Fonts

    • Daniel Berio, Frederic Fol Leymarie, Paul Asente, Jose Echevarria
    • ACM Transactions on GraphicsVolume 41Issue 3Article No.: 28pp 1–21
    • [paper]
  • DG-Font: Deformable Generative Networks for Unsupervised Font Generation

  • Multiple Heads are Better than One:Few-shot Font Generation with Multiple Localized Experts
    • Song Park, Sanghyuk Chun, Junbum Cha, Bado Lee, Hyunjung Shim
    • [paper][code]
    • ICCV 2021

  • Font Style that Fits an Image -- Font Generation Based on Image Context
    • [paper][code]
    • Taiga Miyazono, Brian Kenji Iwana, Daichi Haraguchi, and Seiichi Uchida
    • arxiv 2021

  • Few-shot Font Generation with Localized Style Representations and Factorization

  • Handwritten Chinese Font Generation with Collaborative Stroke Refinement

  • RD-GAN: Few/Zero-Shot Chinese Character Style Transfer via Radical Decomposition and Rendering
    • Yaoxiong Huang, Mengchao He, Lianwen Jin, Yongpan Wang
    • [paper(ECVA)]
    • ECCV 2020

  • Few-shot Compositional Font Generation with Dual Memory
    • Junbum Cha, Sanghyuk Chun, Gayoung Lee, Bado Lee, Seonghyeon Kim, Hwalsuk Lee.
    • [paper] [code]
    • ECCV 2020

  • CalliGAN: Style and Structure-aware Chinese Calligraphy Character Generator
    • [paper][code]
    • Shan-Jean Wu, Chih-Yuan Yang and Jane Yung-jen Hsu
    • AI for Content Creation Workshop CVPR 2020.

  • A Learned Representation for Scalable Vector Graphics
    • [paper]
    • Raphael Gontijo Lopes, David Ha, Douglas Eck, Jonathon Shlens
    • ICCV 2019
    • ICLR workshop 2019

  • Large-scale Tag-based Font Retrieval with Generative Feature Learning

    • [paper][page]
    • Tianlang Chen, Zhaowen Wang, Ning Xu, Hailin Jin, Jiebo Luo
    • ICCV 2019
  • DynTypo: Example-based Dynamic Text Effects Transfer

    • [paper]
    • Yifang Men, Zhouhui Lian, Yingmin Tang, Jianguo Xiao
    • CVPR 2019
    • <iframe width="560" height="315" src="https://www.youtube.com/embed/FkFQ6bV1s-o" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
  • Typography with Decor: Intelligent Text Style Transfer

  • DeepGlyph

  • Coconditional Autoencoding Adversarial Networks for Chinese Font Feature Learning

    • [paper]
    • arxiv 2018
    • Zhizhan Zheng, Feiyun Zhang
  • TET-GAN: Text Effects Transfer via Stylization and Destylization

    • [paper]
    • Shuai Yang, Jiaying Liu, Wenjing Wang, Zongming Guo
    • AAAI2019

  • SCFont: Structure-Guided Chinese Font Generation via Deep Stacked Networks
    • [paper]
    • Yue Jiang, Zhouhui Lian*, Yingmin Tang, Jianguo Xiao
    • AAAI 2019

  • Separating Style and Content for Generalized Style Transfer

    • [paper][code]
    • Yexun Zhang, Ya Zhang, Wenbin Cai
    • CVPR 2018
    • Networks
    • results
  • Deep Learning for Classical Japanese Literature

    • [paper][GitHub]
    • Tarin Clanuwat, Mikel Bober-Irizar, Asanobu Kitamoto, Alex Lamb, Kazuaki Yamamoto, David Ha
    • NeurIPS 2018
    • Kuzushiji-MNIST,Kuzushiji-49 and Kuzushiji-Kanji
    • Kuzushiji-MNIST
    • Kuzushiji-Kanji
  • Multi-Content GAN for Few-Shot Font Style Transfer

    • [code][paper][blog]
    • Azadi, Samaneh, Matthew Fisher, Vladimir Kim, Zhaowen Wang, Eli Shechtman, and Trevor Darrell.
    • CVPR2018
  • Learning to Write Stylized Chinese Characters by Reading a Handful of Examples

    • [paper]
    • Danyang Sun∗, Tongzheng Ren∗, Chongxuan Li, Hang Su†, Jun Zhu†
    • IJCAI 2018
    • SA-VAE

  • DCFont: An End-To-End Deep Chinese Font Generation System
    • [paper]
    • Juncheng Liu, Zhouhui Lian, Jianguo Xiao
    • SIGGRAPH Asia 2017

  • A Book from the Sky 天书: Exploring the Latent Space of Chinese Handwriting

  • Letter Spirit: An Emergent Model of the Perception and Creation of Alphabetic Style
    • [paper]
    • Douglas Hofstadter, Gary McGraw
    • 1993

Other Approach

  • Automatic Generation of Typographic Font from a Small Font Subset

    • [paper]
    • Tomo Miyazaki, Tatsunori Tsuchiya, Yoshihiro Sugaya, Shinichiro Omachi, Masakazu Iwamura, Seiichi Uchida, Koichi Kise
    • 2017

  • FlexyFont: Learning Transferring Rules for Flexible Typeface Synthesis

    • [paper]
    • H. Q. Phan, H. Fu, and A. B. Chan
    • 2015 Computer Graphics Forum

  • Awesome Typography: Statistics-Based Text Effects Transfer

    • [paper][code]
    • Shuai Yang, Jiaying Liu, Zhouhui Lian and Zongming Guo
    • CVPR 2017

  • Easy generation of personal Chinese handwritten fonts

    • [paper]
    • Baoyao Zhou, Weihong Wang, and Zhanghui Chen
    • 2011 ICME(IEEE International Conference on Multimedia and Expo)

  • Automatic shape morphing for Chinese characters

    • [paper]
    • Zhouhui Lian, and Zhouhui Lian
    • SIGGRAPH Asia 2012

Struture Learning

DataSet

  • Deep Learning for Classical Japanese Literature
    • [paper][GitHub]
    • Tarin Clanuwat, Mikel Bober-Irizar, Asanobu Kitamoto, Alex Lamb, Kazuaki Yamamoto, David Ha
    • NeurIPS 2018
    • Kuzushiji-MNIST,Kuzushiji-49 and Kuzushiji-Kanji
    • Kuzushiji-MNIST
    • Kuzushiji-Kanji

Other Application

About

The research collection of typography

https://ishengfang.github.io/TypographyResearchCollection/