GANCCRobotGenerative Adversarial Nets based Chinese Calligraphy Robot
Authors
Organisations
Type | Article |
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Original language | English |
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Pages (from-to) | 474-490 |
Number of pages | 17 |
Journal | Information Sciences |
Volume | 516 |
Early online date | 27 Dec 2019 |
DOI | |
Publication status | Published - 01 Apr 2020 |
Permanent link | Permanent link |
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Abstract
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
Keywords
- calligraphy robot, generative adversarial nets, motion planning
Documents
- GANCCRobot: Generative Adversarial Nets based Chinese Calligraphy Robot
Accepted author manuscript, 2.23 MB, PDF
Licence: CC BY-NC-ND Show licence