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Synthesizing style-preserving cartoons via non-negative style factorization

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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.

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Authors and Affiliations

  1. Institute of Artificial Intelligence, School of Computer Science and Technology, Zhejiang University, Hangzhou, 310027, China

    Zhang Liang, Jun Xiao & Yue-ting Zhuang

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  1. Zhang Liang
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  2. Jun Xiao
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  3. Yue-ting Zhuang
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Correspondence to Jun Xiao.

Additional information

Project supported by the National Basic Research Program (973) of China (No. 2012CB316400), the National Natural Science Foundation of China (No. 60903134), and the Natural Science Foundation of Zhejiang Province, China (No. Y1101129)

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Liang, Z., Xiao, J. & Zhuang, Yt. Synthesizing style-preserving cartoons via non-negative style factorization. J. Zhejiang Univ. - Sci. C 13, 196–207 (2012). https://doi.org/10.1631/jzus.C1100202

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  • DOI: https://doi.org/10.1631/jzus.C1100202

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