CF-Font: Content Fusion for Few-shot Font Generation

• URL: http://arxiv.org/abs/2303.14017v3
• Date: Mon, 15 Apr 2024 08:22:49 GMT
• Title: CF-Font: Content Fusion for Few-shot Font Generation
• Authors: Chi Wang, Min Zhou, Tiezheng Ge, Yuning Jiang, Hujun Bao, Weiwei Xu,
• Abstract summary: 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.
• Score: 63.79915037830131
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Content and style disentanglement is an effective way to achieve few-shot font generation. It allows to transfer the style of the font image in a source domain to the style defined with a few reference images in a target domain. However, the content feature extracted using a representative font might not be optimal. In light of this, we propose a content fusion module (CFM) to project the content feature into a linear space defined by the content features of basis fonts, which can take the variation of content features caused by different fonts into consideration. Our method also allows to optimize the style representation vector of reference images through a lightweight iterative style-vector refinement (ISR) strategy. Moreover, we treat the 1D projection of a character image as a probability distribution and leverage the distance between two distributions as the reconstruction loss (namely projected character loss, PCL). Compared to L2 or L1 reconstruction loss, the distribution distance pays more attention to the global shape of characters. We have evaluated our method on a dataset of 300 fonts with 6.5k characters each. Experimental results verify that our method outperforms existing state-of-the-art few-shot font generation methods by a large margin. The source code can be found at https://github.com/wangchi95/CF-Font.

Related papers

• JoyType: A Robust Design for Multilingual Visual Text Creation [14.441897362967344]
  We introduce a novel approach for multilingual visual text creation, named JoyType. JoyType is designed to maintain the font style of text during the image generation process. Our evaluations, based on both visual and accuracy metrics, demonstrate that JoyType significantly outperforms existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-09-26T04:23:17Z)
• FontDiffuser: One-Shot Font Generation via Denoising Diffusion with Multi-Scale Content Aggregation and Style Contrastive Learning [45.696909070215476]
  FontDiffuser is a diffusion-based image-to-image one-shot font generation method. It consistently excels on complex characters and large style changes compared to previous methods.
  arXiv  Detail & Related papers  (2023-12-19T13:23:20Z)
• Few shot font generation via transferring similarity guided global style and quantization local style [11.817299400850176]
  We present a novel font generation approach by aggregating styles from character similarity-guided global features and stylized component-level representations. Our AFFG method could obtain a complete set of component-level style representations, and also control the global glyph characteristics.
  arXiv  Detail & Related papers  (2023-09-02T05:05:40Z)
• 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)
• Few-shot Font Generation by Learning Style Difference and Similarity [84.76381937516356]
  We propose a novel font generation approach by learning the Difference between different styles and the Similarity of the same style (DS-Font) Specifically, we propose a multi-layer style projector for style encoding and realize a distinctive style representation via our proposed Cluster-level Contrastive Style (CCS) loss.
  arXiv  Detail & Related papers  (2023-01-24T13:57:25Z)
• DSI2I: Dense Style for Unpaired Image-to-Image Translation [70.93865212275412]
  Unpaired exemplar-based image-to-image (UEI2I) translation aims to translate a source image to a target image domain with the style of a target image exemplar. We propose to represent style as a dense feature map, allowing for a finer-grained transfer to the source image without requiring any external semantic information. Our results show that the translations produced by our approach are more diverse, preserve the source content better, and are closer to the exemplars when compared to the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-12-26T18:45:25Z)
• 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)
• XMP-Font: Self-Supervised Cross-Modality Pre-training for Few-Shot Font Generation [13.569449355929574]
  We propose a self-supervised cross-modality pre-training strategy and a cross-modality transformer-based encoder. The encoder is conditioned jointly on the glyph image and the corresponding stroke labels. It only requires one reference glyph and achieves the lowest rate of bad cases in the few-shot font generation task 28% lower than the second best.
  arXiv  Detail & Related papers  (2022-04-11T13:34:40Z)

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.