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Front. Inform. Technol. Electron. Eng.  2012, Vol. 13 Issue (3): 196-207    DOI: 10.1631/jzus.C1100202
    
Synthesizing style-preserving cartoons via non-negative style factorization
Zhang Liang, Jun Xiao, Yue-ting Zhuang
Institute of Artificial Intelligence, School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
Synthesizing style-preserving cartoons via non-negative style factorization
Zhang Liang, Jun Xiao, Yue-ting Zhuang
Institute of Artificial Intelligence, School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
 全文: PDF 
摘要： 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.
关键词： Character cartoon;  Machine learning;  Cartoon synthesis    
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.
Key words: Character cartoon    Machine learning    Cartoon synthesis
收稿日期: 2011-07-08 出版日期: 2012-03-01
CLC:  TP391  
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Zhang Liang
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引用本文:

Zhang Liang, Jun Xiao, Yue-ting Zhuang. Synthesizing style-preserving cartoons via non-negative style factorization. Front. Inform. Technol. Electron. Eng., 2012, 13(3): 196-207.

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http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C1100202    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2012/V13/I3/196

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