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On-line Access: 2012-03-01

Received: 2011-07-08

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


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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%T Synthesizing style-preserving cartoons via non-negative style factorization
%A Zhang Liang
%A Jun Xiao
%A Yue-ting Zhuang
%J Journal of Zhejiang University SCIENCE C
%V 13
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%P 196-207
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%D 2012
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1100202

T1 - Synthesizing style-preserving cartoons via non-negative style factorization
A1 - Zhang Liang
A1 - Jun Xiao
A1 - Yue-ting Zhuang
J0 - Journal of Zhejiang University Science C
VL - 13
IS - 3
SP - 196
EP - 207
%@ 1869-1951
Y1 - 2012
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1100202

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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