mlsanityData to 14 Oct 2026

Impressions2Font: Generating Fonts by Specifying Impressions

arXiv 2103.10036 ↗First posted 18 Mar 2021cs.CVcited by 10

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Abstract (arXiv)

Various fonts give us various impressions, which are often represented by words. This paper proposes Impressions2Font (Imp2Font) that generates font images with specific impressions. Imp2Font is an extended version of conditional generative adversarial networks (GANs). More precisely, Imp2Font accepts an arbitrary number of impression words as the condition to generate the font images. These impression words are converted into a soft-constraint vector by an impression embedding module built on a word embedding technique. Qualitative and quantitative evaluations prove that Imp2Font generates font images with higher quality than comparative methods by providing multiple impression words or even unlearned words.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 15 of its 22 references that are in the map. First posted on arXiv 18 Mar 2021.

Builds on

15 of 22 references.

References · 15

  1. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,474
  2. 2014Conditional Generative Adversarial Netscited by 2,162
  3. 2016Conditional Image Synthesis With Auxiliary Classifier GANscited by 565
  4. 2019Mode Seeking Generative Adversarial Networks for Diverse Image Synthesiscited by 138
  5. 2017Multi-Content GAN for Few-Shot Font Style Transfercited by 100
  6. 2020Few-shot Compositional Font Generation with Dual Memorycited by 44
  7. 2017Auto-Encoder Guided GAN for Chinese Calligraphy Synthesiscited by 29
  8. 2015DeepFont: Identify Your Font from An Imagecited by 24
  9. 2019Large-scale Tag-based Font Retrieval with Generative Feature Learningcited by 22
  10. 2019GlyphGAN: Style-Consistent Font Generation Based on Generative Adversarial Networkscited by 16
  11. 2020Attribute2Font: Creating Fonts You Want From Attributescited by 12
  12. 2020Let Me Choose: From Verbal Context to Font Selectioncited by 8
3 more
  1. 2017Sound-Word2Vec: Learning Word Representations Grounded in Soundscited by 4
  2. 2018Class-Distinct and Class-Mutual Image Generation with GANscited by 4
  3. 2020Effect of Text Color on Word Embeddingscited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 3; 2025: 1; 2026: 2.

10 papers.

  1. 2021Shared Latent Space of Font Shapes and Their Noisy Impressionscited by 7
  2. 2022Font Generation with Missing Impression Labelscited by 6
  3. 2024GRIF-DM: Generation of Rich Impression Fonts using Diffusion Modelscited by 4
  4. 2022Font Shape-to-Impression Translationcited by 3
  5. 2024Impression-CLIP: Contrastive Shape-Impression Embedding for Fontscited by 3
  6. 2026VecGlypher: Unified Vector Glyph Generation with Language Modelscited by 3
  7. 2023Contextual Font Recommendations based on User Intentcited by 1
  8. 2025Embedding Font Impression Word Tags Based on Co-occurrencecited by 1
  9. 2024Font Impression Estimation in the Wildcited by 0
  10. 2026Hierarchical Co-Embedding of Font Shapes and Impression Tagscited by 0