A Two-stage Effect Rendering Method for Art Font Based on CGAN Network

Ye Wu-jian; Gao Hai-jian; Weng Shao-wei; Gao Zhi; Wang Shan-jin; Zhang Chun-yu; Liu Yi-jun

doi:10.12052/gdutxb.180162

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

A Two-stage Effect Rendering Method for Art Font Based on CGAN Network

更新时间：2026-05-27

- A Two-stage Effect Rendering Method for Art Font Based on CGAN Network
- Vol. 36, Issue 3, Pages: 47-55(2019)
- 作者机构：
  
  1. 广东工业大学　信息工程学院,广东,广州,510006
  2. 东莞理工学院　电子工程与智能化学院,广东,东莞,523808
  3. 西藏民族大学　信息工程学院,陕西,咸阳,712082
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.180162
  CLC： TP391
- Online First：09 May 2019，
  
  Published：2019
- 稿件说明：
移动端阅览
叶武剑, 高海健, 翁韶伟, 高智, 王善进, 张春玉, 刘怡俊. 基于CGAN网络的二阶段式艺术字体渲染方法[J]. 广东工业大学学报, 2019, 36(3): 47-55.

Ye Wu-jian, Gao Hai-jian, Weng Shao-wei, et al. A Two-stage Effect Rendering Method for Art Font Based on CGAN Network[J]. 2019, 36(3): 47-55.
叶武剑, 高海健, 翁韶伟, 高智, 王善进, 张春玉, 刘怡俊. 基于CGAN网络的二阶段式艺术字体渲染方法[J]. 广东工业大学学报, 2019, 36(3): 47-55. DOI： 10.12052/gdutxb.180162.

Ye Wu-jian, Gao Hai-jian, Weng Shao-wei, et al. A Two-stage Effect Rendering Method for Art Font Based on CGAN Network[J]. 2019, 36(3): 47-55. DOI： 10.12052/gdutxb.180162.

摘要

艺术字体渲染是媒体排版的重要技术之一. 如何提供一种高效的艺术字体渲染方法来实现生成艺术字体的特效多样化与清晰化是亟待解决的问题. 本文借助条件生成式对抗网络(Conditional Generative Adversarial Networks

CGAN)

提出一个包括风格化处理和清晰化处理的二阶段式艺术字体渲染方法

对字体实现高效的特定效果渲染. 首先

风格化处理是通过构建风格化网络模型对多样化的字体进行各种不同的2D或3D特效渲染; 然后

构建清晰化网络模型对生成的艺术字体图进行清晰化处理

这克服了单一GAN网络生成图模糊的缺陷. 实验结果表明

二阶段式艺术字体渲染方法所生成的特效字体的纹理细节较为丰富

不受限于文字骨架

而且字体清晰度也得到较大提升; 同时

该方法对字体的特效渲染批量化处理效率也明显提高

具有较强实用价值.

Abstract

Art font rendering is one of the important techniques of media typesetting. How to provide an efficient art font rendering method to realize the diversity and clearness of the special effects of generative art fonts is an urgent problem for technicians in this field. With the help of Conditional Generative Adversarial Networks (CGAN)

a two-stage art font rendering method that includes style transfer processing and enhancement processing is proposed to achieve high quality rendering of art fonts. First

by style transfer processing

a stylized network model is built to render various 2D or 3D special effects on a variety of input fonts. Then

by enhancement processing

a sharpening network model is built to sharpen the generated art font images

which overcomes the defect of fuzzy image generation in a single GAN network. The experimental results show that

comparing with existing methods

this scheme has a better performance for generating more diverse and more clear special art fonts

whose texture details are relatively rich

and are not limited to the text skeleton. At the same time

this scheme can also improve the efficiency of art font rendering in the batch processing and has a higher practical value.

关键词

Keywords

references

YANG S, LIU J, LIAN Z, et al . Awesome typography: statistics-based text effects transfer [J ] . IEEE Computer Vision and Pattern Recognition, 2017: 7464-7 473.

BARNES C, SHECHTMAN E, GOLDMAN D B, et al. The generalized patchmatch correspondence algorithm[C]// European Conference on Computer Vision. Berlin, Heidelberg: Springer, 2010: 29-43.

BARNES C, SHECHTMAN E, FINKELSTEIN A, et al. PatchMatch: a randomized correspondence algorithm for structural image editing[J]. ACM Transactions on Graphics (ToG), 2009, 28(3): 24.

GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets[C]//International Conference on Neural Information Processing Systems. Canada, Montreal: MIT Press, 2014: 2672-2680.

ARJOVSKY M, CHINTALA S, BOTTOU L. Wasserstein generative adversarial networks [C]//International Conference on Machine Learning. Sydney,Australia:ICML,2017: 214-223

CHANG J, SCHERER S. Learning representations of emotional speech with deep convolutional generative adversarial networks[C]//2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). New Orleans: IEEE, 2017: 2746-2750.

ISOLA P, ZHU J Y, ZHOU T, et al. Image-to-image translation with conditional adversarial networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. Honolulu, Hawaii: IEEE, 2017: 1125-1134.

ZHU J Y, PARK T, ISOLA P, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks[C]//Proceedings of the IEEE International Conference on Computer Vision. Italy, Venice: IEEE, 2017: 2223-2232.

ZHU J Y, KRÄHENBÜHL, PHILIPP, SHECHTMAN E, et al . Generative visual manipulation on the natural image manifold [J ] . Journal of Zhejiang University-Science C(Computers Electronics), 2016, 15(4): 312-320.

ZENG B, REN W L, CHEN Y H. An unpaired face illumination normalization method based on cycleGAN [J]. Journal of Guangdong University of Technology, 2018, 35(5): 11-19.

LI Y, FANG C, YANG J, et al. Universal style transfer via feature transforms[C]// Conference and Workshop on Neural Information Processing Systems. Long Beach: NIPS, 2017: 386-396.

JOHNSON J, ALAHI A, FEI-FEI L. Perceptual losses for real-time style transfer and super-resolution[C]// European conference on computer vision. Amsterdam: Springer, 2016: 694-711.

GATYS L A, ECKER A S, BETHGE M. Image style transfer using convolutional neural networks[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 2414-2423.

HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. Boston, Massachusetts: IEEE, 2016: 770-778.

SZEGEDY C, VANHOUCKE V, IOFFE S, et al. Rethinking the inception architecture for computer vision[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. Las Vegas: IEEE, 2016: 2818-2826.

SZEGEDY C, IOFFE S, VANHOUCKE V, et al. Inception-v4, inception-resnet and the impact of residual connections on learning[C]//Thirty-First AAAI Conference on Artificial Intelligence. San Francisco, California: AAAI, 2017: 4278-4284.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

A Resource Allocation Optimization Method for Multi-UAV Integrated Sensing, Computing, and Communication Towards Disaster Relief

Research and Implementation of a Pulsed Laser Ranging Method Based on Digital Mixing

Robust Design for Artificial Noise-assisted UAV Terahertz Secure Communication

A Study of Power Quality Regulation Strategies of Energy Storage Converters

Modeling of 3D NAND Memory Cells with Taper Angle and Programming Strategy Optimization

Related Author

No data

Related Institution

No data

AI问答

⁰