Multiform Fonts-to-Fonts Translation via Style and Content Disentangled Representations of Chinese Character

• URL: http://arxiv.org/abs/2004.03338v1
• Date: Sat, 28 Mar 2020 04:30:00 GMT
• Title: Multiform Fonts-to-Fonts Translation via Style and Content Disentangled Representations of Chinese Character
• Authors: Fenxi Xiao, Jie Zhang, Bo Huang, Xia Wu
• Abstract summary: The main purpose of this paper is to design a network framework that can extract and recombine the content and style of the characters. The paper combines various depth networks such as Convolutional Neural Network, Multi-layer Perceptron and Residual Network to find the optimal model. The result shows that those characters we have generated is very close to real characters, using Structural Similarity index and Peak Signal-to-Noise Ratio evaluation criterions.
• Score: 10.236778478360614
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper mainly discusses the generation of personalized fonts as the problem of image style transfer. The main purpose of this paper is to design a network framework that can extract and recombine the content and style of the characters. These attempts can be used to synthesize the entire set of fonts with only a small amount of characters. The paper combines various depth networks such as Convolutional Neural Network, Multi-layer Perceptron and Residual Network to find the optimal model to extract the features of the fonts character. The result shows that those characters we have generated is very close to real characters, using Structural Similarity index and Peak Signal-to-Noise Ratio evaluation criterions.

Related papers

• Decoupling Layout from Glyph in Online Chinese Handwriting Generation [6.566541829858544]
  We develop a text line layout generator and stylized font synthesizer. The layout generator performs in-context-like learning based on the text content and the provided style references to generate positions for each glyph autoregressively. The font synthesizer which consists of a character embedding dictionary, a multi-scale calligraphy style encoder, and a 1D U-Net based diffusion denoiser will generate each font on its position while imitating the calligraphy style extracted from the given style references.
  arXiv  Detail & Related papers  (2024-10-03T08:46:17Z)
• VQ-Font: Few-Shot Font Generation with Structure-Aware Enhancement and Quantization [52.870638830417]
  We propose a VQGAN-based framework (i.e., VQ-Font) to enhance glyph fidelity through token prior refinement and structure-aware enhancement. Specifically, we pre-train a VQGAN to encapsulate font token prior within a codebook. Subsequently, VQ-Font refines the synthesized glyphs with the codebook to eliminate the domain gap between synthesized and real-world strokes.
  arXiv  Detail & Related papers  (2023-08-27T06:32:20Z)
• CF-Font: Content Fusion for Few-shot Font Generation [63.79915037830131]
  We propose a content fusion module (CFM) to project the content feature into a linear space defined by the content features of basis fonts. Our method also allows to optimize the style representation vector of reference images. We have evaluated our method on a dataset of 300 fonts with 6.5k characters each.
  arXiv  Detail & Related papers  (2023-03-24T14:18:40Z)
• Diff-Font: Diffusion Model for Robust One-Shot Font Generation [110.45944936952309]
  We propose a novel one-shot font generation method based on a diffusion model, named Diff-Font. The proposed model aims to generate the entire font library by giving only one sample as the reference. The well-trained Diff-Font is not only robust to font gap and font variation, but also achieved promising performance on difficult character generation.
  arXiv  Detail & Related papers  (2022-12-12T13:51:50Z)
• Few-Shot Font Generation by Learning Fine-Grained Local Styles [90.39288370855115]
  Few-shot font generation (FFG) aims to generate a new font with a few examples. We propose a new font generation approach by learning 1) the fine-grained local styles from references, and 2) the spatial correspondence between the content and reference glyphs.
  arXiv  Detail & Related papers  (2022-05-20T05:07:05Z)
• Scalable Font Reconstruction with Dual Latent Manifolds [55.29525824849242]
  We propose a deep generative model that performs typography analysis and font reconstruction. Our approach enables us to massively scale up the number of character types we can effectively model. We evaluate on the task of font reconstruction over various datasets representing character types of many languages.
  arXiv  Detail & Related papers  (2021-09-10T20:37:43Z)
• A Multi-Implicit Neural Representation for Fonts [79.6123184198301]
  font-specific discontinuities like edges and corners are difficult to represent using neural networks. We introduce textitmulti-implicits to represent fonts as a permutation-in set of learned implict functions, without losing features.
  arXiv  Detail & Related papers  (2021-06-12T21:40:11Z)
• Few-shot Font Generation with Localized Style Representations and Factorization [23.781619323447003]
  We propose a novel font generation method by learning localized styles, namely component-wise style representations, instead of universal styles. Our method shows remarkably better few-shot font generation results (with only 8 reference glyph images) than other state-of-the-arts.
  arXiv  Detail & Related papers  (2020-09-23T10:33:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.