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Generative adversarial networks based motion learning towards robotic calligraphy synthesis.

Authors :
Wang, Xiaoming
Yang, Yilong
Wang, Weiru
Zhou, Yuanhua
Yin, Yongfeng
Gong, Zhiguo
Source :
CAAI Transactions on Intelligence Technology; Apr2024, Vol. 9 Issue 2, p452-466, 15p
Publication Year :
2024

Abstract

Robot calligraphy visually reflects the motion capability of robotic manipulators. While traditional researches mainly focus on image generation and the writing of simple calligraphic strokes or characters, this article presents a generative adversarial network (GAN)‐based motion learning method for robotic calligraphy synthesis (Gan2CS) that can enhance the efficiency in writing complex calligraphy words and reproducing classic calligraphy works. The key technologies in the proposed approach include: (1) adopting the GAN to learn the motion parameters from the robot writing operation; (2) converting the learnt motion data into the style font and realising the transition from static calligraphy images to dynamic writing demonstration; (3) reproducing high‐precision calligraphy works by synthesising the writing motion data hierarchically. In this study, the motion trajectories of sample calligraphy images are firstly extracted and converted into the robot module. The robot performs the writing with motion planning, and the writing motion parameters of calligraphy strokes are learnt with GANs. Then the motion data of basic strokes is synthesised based on the hierarchical process of 'stroke‐radical‐part‐character'. And the robot re‐writes the synthesised characters whose similarity with the original calligraphy characters is evaluated. Regular calligraphy characters have been tested in the experiments for method validation and the results validated that the robot can actualise the robotic calligraphy synthesis of writing motion data with GAN. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
24682322
Volume :
9
Issue :
2
Database :
Complementary Index
Journal :
CAAI Transactions on Intelligence Technology
Publication Type :
Academic Journal
Accession number :
176690759
Full Text :
https://doi.org/10.1049/cit2.12198