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MaLiang: An Emotion-driven Chinese Calligraphy Artwork Composition System

Published: 12 October 2020 Publication History

Abstract

We present a novel Chinese calligraphy artwork composition system (MaLiang) which can generate aesthetic, stylistic and diverse calligraphy images based on the emotion status from the input text. Different from previous research, it's the first work to endow the calligraphy synthesis with the ability to express fickle emotions and composite a whole piece of discourse-level calligraphy artwork instead of single character images. The system consists of three modules: emotion detection, character image generation, and layout prediction. As a creative form of interactive art, MaLiang has been exhibited in several famous international art festivals.

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This is the presentation our MM 2020 work. We present a novel Chinese calligraphy artwork composition system (MaLiang) which can generate aesthetic, stylistic and diverse calligraphy images based on the emotion status from the input text. Different from previous research, it?s the first work to endow the calligraphy synthesis with the ability to express fickle emotions and composite a whole piece of discourse-level calligraphy artwork instead of single character images. The system consists of three modules: emotion detection, character image generation, and layout prediction. As a creative form of interactive art, MaLiang has been exhibited in several famous international art festivals.

References

[1]
Bo Chang, Qiong Zhang, Shenyi Pan, and Lili Meng. 2018. Generating handwritten chinese characters using cyclegan. In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 199--207.
[2]
Huimin Chen, Xiaoyuan Yi, Maosong Sun, Wenhao Li, Cheng Yang, and Zhipeng Guo. 2019. Sentiment-controllable Chinese poetry generation. In Proceedings of the 28th International Joint Conference on Artificial Intelligence.
[3]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.
[4]
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in neural information processing systems. 2672--2680.
[5]
Junho Kim, Minjae Kim, Hyeonwoo Kang, and Kwang Hee Lee. 2020. U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation. In International Conference on Learning Representations.
[6]
Yuchen Tian. 2017. Master Chinese calligraphy with conditional adversarial networks. https://github.com/kaonashi-tyc/zi2zi.

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  • (2024)Future Ink: The Collision of AI and Chinese CalligraphyJournal on Computing and Cultural Heritage 10.1145/3700882Online publication date: 26-Nov-2024

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cover image ACM Conferences
MM '20: Proceedings of the 28th ACM International Conference on Multimedia
October 2020
4889 pages
ISBN:9781450379885
DOI:10.1145/3394171
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 October 2020

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Author Tags

  1. GAN
  2. calligraphy generation
  3. interactive art

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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  • (2024)Future Ink: The Collision of AI and Chinese CalligraphyJournal on Computing and Cultural Heritage 10.1145/3700882Online publication date: 26-Nov-2024

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