Journal of Zhejiang University SCIENCE C

, Volume 13, Issue 3, pp 196–207 | Cite as

Synthesizing style-preserving cartoons via non-negative style factorization



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 

CLC number



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Copyright information

© Journal of Zhejiang University Science Editorial Office and Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Institute of Artificial Intelligence, School of Computer Science and TechnologyZhejiang UniversityHangzhouChina

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