Synthesizing style-preserving cartoons via non-negative style factorization

作     者：Zhang LIANG Jun XIAO Yue-ting ZHUANG 

作者机构：Institute of Artificial IntelligenceSchool of Computer Science and TechnologyZhejiang UniversityHangzhou 310027China 

出 版 物：《Journal of Zhejiang University-Science C(Computers and Electronics)》 (浙江大学学报C辑（计算机与电子（英文版）)

年 卷 期：2012年第13卷第3期

页      面：196-207页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

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) 

主　　题：Character cartoon Machine learning Cartoon synthesis 

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.