Synthesizing style-preserving cartoons via non-negative style factorization  

作　　者：Zhang LIANG Jun XIAO Yue-ting ZHUANG 

机构地区：[1]Institute of Artificial Intelligence,School of Computer Science and Technology,Zhejiang University,Hangzhou 310027,China

出　　处：《Journal of Zhejiang University-Science C(Computers and Electronics)》2012年第3期196-207,共12页浙江大学学报C辑（计算机与电子（英文版）

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

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 reynthesize 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.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 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]