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

• URL: http://arxiv.org/abs/2110.06688v1
• Date: Wed, 13 Oct 2021 12:57:19 GMT
• Title: DeepVecFont: Synthesizing High-quality Vector Fonts via Dual-modality Learning
• Authors: Yizhi Wang and Zhouhui Lian
• Abstract summary: We propose a novel method, DeepVecFont, to generate visually-pleasing vector glyphs. The highlights of this paper are threefold. First, we design a dual-modality learning strategy which utilizes both image-aspect and sequence-aspect features of fonts to synthesize vector glyphs. Second, we provide a new generative paradigm to handle unstructured data (e.g., vector glyphs) by randomly sampling plausible results to get the optimal one which is further refined under the guidance of generated structured data.
• Score: 21.123297001902177
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatic font generation based on deep learning has aroused a lot of interest in the last decade. However, only a few recently-reported approaches are capable of directly generating vector glyphs and their results are still far from satisfactory. In this paper, we propose a novel method, DeepVecFont, to effectively resolve this problem. Using our method, for the first time, visually-pleasing vector glyphs whose quality and compactness are both comparable to human-designed ones can be automatically generated. The key idea of our DeepVecFont is to adopt the techniques of image synthesis, sequence modeling and differentiable rasterization to exhaustively exploit the dual-modality information (i.e., raster images and vector outlines) of vector fonts. The highlights of this paper are threefold. First, we design a dual-modality learning strategy which utilizes both image-aspect and sequence-aspect features of fonts to synthesize vector glyphs. Second, we provide a new generative paradigm to handle unstructured data (e.g., vector glyphs) by randomly sampling plausible synthesis results to get the optimal one which is further refined under the guidance of generated structured data (e.g., glyph images). Finally, qualitative and quantitative experiments conducted on a publicly-available dataset demonstrate that our method obtains high-quality synthesis results in the applications of vector font generation and interpolation, significantly outperforming the state of the art.

Related papers

• HFH-Font: Few-shot Chinese Font Synthesis with Higher Quality, Faster Speed, and Higher Resolution [17.977410216055024]
  We introduce HFH-Font, a few-shot font synthesis method capable of efficiently generating high-resolution glyph images. For the first time, large-scale Chinese vector fonts of a quality comparable to those manually created by professional font designers can be automatically generated.
  arXiv  Detail & Related papers  (2024-10-09T02:30:24Z)
• DeepCalliFont: Few-shot Chinese Calligraphy Font Synthesis by Integrating Dual-modality Generative Models [20.76773399161289]
  Few-shot font generation, especially for Chinese calligraphy fonts, is a challenging and ongoing problem. We propose a novel model, DeepCalliFont, for few-shot Chinese calligraphy font synthesis by integrating dual-modality generative models.
  arXiv  Detail & Related papers  (2023-12-16T04:23:12Z)
• 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)
• DualVector: Unsupervised Vector Font Synthesis with Dual-Part Representation [43.64428946288288]
  Current font synthesis methods fail to represent the shape concisely or require vector supervision during training. We propose a novel dual-part representation for vector glyphs, where each glyph is modeled as a collection of closed "positive" and "negative" path pairs. Our method, named Dual-of-Font-art, outperforms state-of-the-art methods for practical use.
  arXiv  Detail & Related papers  (2023-05-17T08:18:06Z)
• DeepVecFont-v2: Exploiting Transformers to Synthesize Vector Fonts with Higher Quality [38.32966391626858]
  This paper proposes an enhanced version of DeepVecFont for vector font synthesis. We adopt Transformers instead of RNNs to process sequential data and design a relaxation representation for vector outlines. We also propose to sample auxiliary points in addition to control points to precisely align the generated and target B'ezier curves or lines.
  arXiv  Detail & Related papers  (2023-03-25T23:28:19Z)
• VecFontSDF: Learning to Reconstruct and Synthesize High-quality Vector Fonts via Signed Distance Functions [15.47282857047361]
  This paper proposes an end-to-end trainable method, VecFontSDF, to reconstruct and synthesize high-quality vector fonts. Based on the proposed SDF-based implicit shape representation, VecFontSDF learns to model each glyph as shape primitives enclosed by several parabolic curves.
  arXiv  Detail & Related papers  (2023-03-22T16:14:39Z)
• 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)
• 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)
• Font Completion and Manipulation by Cycling Between Multi-Modality Representations [113.26243126754704]
  We innovate to explore the generation of font glyphs as 2D graphic objects with the graph as an intermediate representation. We formulate a cross-modality cycled image-to-image structure with a graph between an image encoder and an image. Our model generates improved results than both image-to-image baseline and previous state-of-the-art methods for glyph completion.
  arXiv  Detail & Related papers  (2021-08-30T02:43:29Z)
• Towards Open-World Text-Guided Face Image Generation and Manipulation [52.83401421019309]
  We propose a unified framework for both face image generation and manipulation. Our method supports open-world scenarios, including both image and text, without any re-training, fine-tuning, or post-processing.
  arXiv  Detail & Related papers  (2021-04-18T16:56:07Z)

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.