mlsanityData to 7 Oct 2026

Learning Implicit Glyph Shape Representation

arXiv 2106.08573 ↗First posted 16 Jun 2021cs.CVcited by 7

Not on the map. Why?

Abstract (arXiv)

In this paper, we present a novel implicit glyph shape representation, which models glyphs as shape primitives enclosed by quadratic curves, and naturally enables generating glyph images at arbitrary high resolutions. Experiments on font reconstruction and interpolation tasks verified that this structured implicit representation is suitable for describing both structure and style features of glyphs. Furthermore, based on the proposed representation, we design a simple yet effective disentangled network for the challenging one-shot font style transfer problem, and achieve the best results comparing to state-of-the-art alternatives in both quantitative and qualitative comparisons. Benefit from this representation, our generated glyphs have the potential to be converted to vector fonts through post-processing, reducing the gap between rasterized images and vector graphics. We hope this work can provide a powerful tool for 2D shape analysis and synthesis, and inspire further exploitation in implicit representations for 2D shape modeling.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 22 of its 53 references that are in the map. First posted on arXiv 16 Jun 2021.

Builds on

22 of 53 references.

References · 22

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  5. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  6. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  7. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,682
  8. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,037
  9. 2018Learning Implicit Fields for Generative Shape Modelingcited by 1,060
  10. 2019BSP-Net: Generating Compact Meshes via Binary Space Partitioningcited by 205
  11. 2019CvxNet: Learnable Convex Decompositioncited by 173
  12. 2020DeepSVG: A Hierarchical Generative Network for Vector Graphics Animationcited by 158
10 more
  1. 2021Im2Vec: Synthesizing Vector Graphics without Vector Supervisioncited by 114
  2. 2017Multi-Content GAN for Few-Shot Font Style Transfercited by 100
  3. 2019A Learned Representation for Scalable Vector Graphicscited by 91
  4. 2019BAE-NET: Branched Autoencoder for Shape Co-Segmentationcited by 89
  5. 2019Artistic Glyph Image Synthesis via One-Stage Few-Shot Learningcited by 53
  6. 2019STEFANN: Scene Text Editor using Font Adaptive Neural Networkcited by 46
  7. 2019Deep Parametric Shape Predictions using Distance Fieldscited by 30
  8. 2021A Multi-Implicit Neural Representation for Fontscited by 21
  9. 2018TET-GAN: Text Effects Transfer via Stylization and Destylizationcited by 19
  10. 2019GlyphGAN: Style-Consistent Font Generation Based on Generative Adversarial Networkscited by 16

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 0; 2023: 4; 2024: 2.

7 papers.

  1. 2023DeepVecFont-v2: Exploiting Transformers to Synthesize Vector Fonts with Higher Qualitycited by 27
  2. 2023DualVector: Unsupervised Vector Font Synthesis with Dual-Part Representationcited by 16
  3. 2024Kinetic Typography Diffusion Modelcited by 9
  4. 2023VecFontSDF: Learning to Reconstruct and Synthesize High-quality Vector Fonts via Signed Distance Functionscited by 7
  5. 2023AmbiGen: Generating Ambigrams from Pre-trained Diffusion Modelcited by 2
  6. 2021Natural Scene Text Editing Based on AIcited by 0
  7. 2024Total Disentanglement of Font Images into Style and Character Class Featurescited by 0