TY - GEN
T1 - Chinese Calligraphy Style Transfer with Generative Adversarial Network
AU - Dai, Jiani
N1 - Publisher Copyright:
© 2024 ACM.
PY - 2024/6/28
Y1 - 2024/6/28
N2 - This manuscript delineates an innovative approach for automating the emulation and creation of Chinese calligraphy styles using Generative Adversarial Networks (GANs). The proposed framework CCUT consists of two main elements: A content supplementation network and a style transfer network. These components work collaboratively to learn from two of Chinese calligraphy masters, Zhao Mengfu and Yan Zhenqin, and transform standard Zheng Kai font into a unique calligraphy style while retaining the essential textual content. The objective of this research is to reduce manual labor involved in the transfer of calligraphic styles and to investigate its prospective applications in the development of fonts. Additionally, we conduct a comprehensive evaluation of the outcomes generated by our and others' models in comparison to human calligraphy, offering an in-depth analysis of the discrepancies noted.
AB - This manuscript delineates an innovative approach for automating the emulation and creation of Chinese calligraphy styles using Generative Adversarial Networks (GANs). The proposed framework CCUT consists of two main elements: A content supplementation network and a style transfer network. These components work collaboratively to learn from two of Chinese calligraphy masters, Zhao Mengfu and Yan Zhenqin, and transform standard Zheng Kai font into a unique calligraphy style while retaining the essential textual content. The objective of this research is to reduce manual labor involved in the transfer of calligraphic styles and to investigate its prospective applications in the development of fonts. Additionally, we conduct a comprehensive evaluation of the outcomes generated by our and others' models in comparison to human calligraphy, offering an in-depth analysis of the discrepancies noted.
KW - Calligraphy
KW - Deep Learning
KW - Generative Adversarial Networks
KW - Image-To-image Translation
KW - Style Transfer
UR - https://www.scopus.com/pages/publications/85202829071
U2 - 10.1145/3677454.3677471
DO - 10.1145/3677454.3677471
M3 - 会议稿件
AN - SCOPUS:85202829071
T3 - ACM International Conference Proceeding Series
SP - 104
EP - 108
BT - ARAEML 2024 - 2024 International Conference on Advanced Robotics, Automation Engineering and Machine Learning
PB - Association for Computing Machinery
T2 - 2024 International Conference on Advanced Robotics, Automation Engineering and Machine Learning, ARAEML 2024
Y2 - 28 June 2024 through 30 June 2024
ER -