publication . Preprint . 2018

Unsupervised Typography Transfer

Sun, Hanfei; Luo, Yiming; Lu, Ziang;
Open Access English
  • Published: 07 Feb 2018
Abstract
Traditional methods in Chinese typography synthesis view characters as an assembly of radicals and strokes, but they rely on manual definition of the key points, which is still time-costing. Some recent work on computer vision proposes a brand new approach: to treat every Chinese character as an independent and inseparable image, so the pre-processing and post-processing of each character can be avoided. Then with a combination of a transfer network and a discriminating network, one typography can be well transferred to another. Despite the quite satisfying performance of the model, the training process requires to be supervised, which means in the training data...
Subjects
free text keywords: Computer Science - Computer Vision and Pattern Recognition
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[1] Jie Chang and Yujun Gu. Chinese typography transfer. 07 2017.

[2] Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros. Image-to-image translation with conditional adversarial networks. 11 2016.

[3] Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge. Image style transfer using convolutional neural networks. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016. [OpenAIRE]

[4] Yaniv Taigman, Adam Polyak, and Lior Wolf. Unsupervised cross-domain image generation. 11 2016.

[5] Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A. Efros. Unpaired image-to-image translation using cycle-consistent adversarial networks. 03 2017.

[6] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. CoRR, abs/1505.04597, 2015. [OpenAIRE]

[7] Vincent Dumoulin, Jonathon Shlens, and Manjunath Kudlur. A learned representation for artistic style. CoRR, abs/1610.07629, 2016. [OpenAIRE]

[8] Tong Che, Yanran Li, Athul Paul Jacob, Yoshua Bengio, and Wenjie Li. Mode regularized generative adversarial networks. CoRR, abs/1612.02136, 2016. [OpenAIRE]

[9] Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron C. Courville, Ruslan Salakhutdinov, Richard S. Zemel, and Yoshua Bengio. Show, attend and tell: Neural image caption generation with visual attention. CoRR, abs/1502.03044, 2015.

[10] Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, and Olivier Bousquet. Are gans created equal? a large-scale study. arXiv preprint arXiv:1711.10337, 2017. [OpenAIRE]

Abstract
Traditional methods in Chinese typography synthesis view characters as an assembly of radicals and strokes, but they rely on manual definition of the key points, which is still time-costing. Some recent work on computer vision proposes a brand new approach: to treat every Chinese character as an independent and inseparable image, so the pre-processing and post-processing of each character can be avoided. Then with a combination of a transfer network and a discriminating network, one typography can be well transferred to another. Despite the quite satisfying performance of the model, the training process requires to be supervised, which means in the training data...
Subjects
free text keywords: Computer Science - Computer Vision and Pattern Recognition
Download from

[1] Jie Chang and Yujun Gu. Chinese typography transfer. 07 2017.

[2] Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros. Image-to-image translation with conditional adversarial networks. 11 2016.

[3] Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge. Image style transfer using convolutional neural networks. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016. [OpenAIRE]

[4] Yaniv Taigman, Adam Polyak, and Lior Wolf. Unsupervised cross-domain image generation. 11 2016.

[5] Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A. Efros. Unpaired image-to-image translation using cycle-consistent adversarial networks. 03 2017.

[6] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. CoRR, abs/1505.04597, 2015. [OpenAIRE]

[7] Vincent Dumoulin, Jonathon Shlens, and Manjunath Kudlur. A learned representation for artistic style. CoRR, abs/1610.07629, 2016. [OpenAIRE]

[8] Tong Che, Yanran Li, Athul Paul Jacob, Yoshua Bengio, and Wenjie Li. Mode regularized generative adversarial networks. CoRR, abs/1612.02136, 2016. [OpenAIRE]

[9] Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron C. Courville, Ruslan Salakhutdinov, Richard S. Zemel, and Yoshua Bengio. Show, attend and tell: Neural image caption generation with visual attention. CoRR, abs/1502.03044, 2015.

[10] Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, and Olivier Bousquet. Are gans created equal? a large-scale study. arXiv preprint arXiv:1711.10337, 2017. [OpenAIRE]

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