GANCCRobot: Generative adversarial nets based chinese calligraphy robot
GANCCRobot: Generative adversarial nets based chinese calligraphy robot
复制标题
GANCCRobot：基于生成对抗网络的中国书法机器人
DOI：
10.1016/j.ins.2019.12.079
复制
发表时间：
2020-04
影响因子：
8.1
通讯作者：
Changjing Shang
中科院分区：
文献类型：
--
作者：
Ruiqi Wu;Changle Zhou;Fei Chao;Longzhi Yang;Chih-Min Lin;Changjing Shang
机器人书法作为机器人运动规划的典型应用，对于书法文化的传承和教育具有重要意义。这种机器人的现有实现通常受到其有限的字体生成和评估能力的影响，导致写作风格多样性和写作质量差。本文提出了一种基于生成对抗网络（GAN）的书法机器人框架来解决这种限制。使用这种框架实现的机器人能够学习书写基本的汉字笔画，具有丰富的多样性和接近人类水平的良好质量，而不需要专门设计的评价函数，这要归功于采用修改后的GAN。特别地，笔划的类型信息被引入作为条件信息，并且潜码被应用以最大化所生成的笔划的风格质量。实验结果表明，所提出的模型，使书法机器人能够成功地写基本的中国笔画基于一个给定的类型和风格，具有良好的整体质量。虽然在本报告中使用书法书写对所提出的模型进行了评估，但基础研究很容易适用于许多其他应用，例如机器人涂鸦和字符风格转换。
Robotic calligraphy, as a typical application of robot movement planning, is of great significance for the inheritance and education of calligraphy culture. The existing implementations of such robots often suffer from its limited ability for font generation and evaluation, leading to poor writing style diversity and writing quality. This paper proposes a calligraphic robotic framework based on the generative adversarial nets (GAN) to address such limitation. The robot implemented using such framework is able to learn to write fundamental Chinese character strokes with rich diversities and good quality that is close to the human level, without the requirement of specifically designed evaluation functions thanks to the employment of the revised GAN. In particular, the type information of the stroke is introduced as condition information, and the latent codes are applied to maximize the style quality of the generated strokes. Experimental results demonstrate that the proposed model enables a calligraphic robot to successfully write fundamental Chinese strokes based on a given type and style, with overall good quality. Although the proposed model was evaluated in this report using calligraphy writing, the underpinning research is readily applicable to many other applications, such as robotic graffiti and character style conversion.
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影响因子：
9.2
作者：
Hiatt, Laura M.;Narber, Cody;Trafton, J. Gregory
通讯作者：
Trafton, J. Gregory
影响因子：
1.5
作者：
Y. Sun;Z. Wang;C. Zhou;M. Jiang （江敏）
通讯作者：
M. Jiang （江敏）
DOI：
--
发表时间：
2010
期刊：
Mechanical Engineering & Automation
影响因子：
--
作者：
Zha Xin-wei
通讯作者：
Zha Xin-wei
DOI：
10.1109/icicis.2010.5534678
发表时间：
2010-06
期刊：
The 3rd International Conference on Information Sciences and Interaction Sciences
影响因子：
--
作者：
Yongkui Man;Chunyuan Bian;Hongbin Zhao;Changcheng Xu;S. Ren
通讯作者：
Yongkui Man;Chunyuan Bian;Hongbin Zhao;Changcheng Xu;S. Ren
DOI：
10.1109/icnsc.2006.1673120
发表时间：
2006-08
期刊：
2006 IEEE International Conference on Networking, Sensing and Control
影响因子：
--
作者：
Fenghui Yao;Guifeng Shao
通讯作者：
Fenghui Yao;Guifeng Shao