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On-line Access: 2024-08-27

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Journal of Zhejiang University SCIENCE C 2012 Vol.13 No.3 P.196-207

http://doi.org/10.1631/jzus.C1100202


Synthesizing style-preserving cartoons via non-negative style factorization


Author(s):  Zhang Liang, Jun Xiao, Yue-ting Zhuang

Affiliation(s):  Institute of Artificial Intelligence, School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   liangzhang@zju.edu.cn, junx@zju.edu.cn, yzhuang@zju.edu.cn

Key Words:  Character cartoon, Machine learning, Cartoon synthesis


Zhang Liang, Jun Xiao, Yue-ting Zhuang. Synthesizing style-preserving cartoons via non-negative style factorization[J]. Journal of Zhejiang University Science C, 2012, 13(3): 196-207.

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author="Zhang Liang, Jun Xiao, Yue-ting Zhuang",
journal="Journal of Zhejiang University Science C",
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pages="196-207",
year="2012",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1100202"
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%A Yue-ting Zhuang
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%DOI 10.1631/jzus.C1100202

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T1 - Synthesizing style-preserving cartoons via non-negative style factorization
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C1100202


Abstract: 
We present a complete framework for synthesizing style-preserving 2D cartoons by learning from traditional Chinese cartoons. In contrast to reusing-based approaches which rely on rearranging or retrieving existing cartoon sequences, we aim to generate stylized cartoons with the idea of style factorization. Specifically, starting with 2D skeleton features of cartoon characters extracted by an improved rotoscoping system, we present a non-negative style factorization (NNSF) algorithm to obtain style basis and weights and simultaneously preserve class separability. Thus, factorized style basis can be combined with heterogeneous weights to re-synthesize style-preserving features, and then these features are used as the driving source in the character reshaping process via our proposed subkey-driving strategy. Extensive experiments and examples demonstrate the effectiveness of the proposed framework.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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