Synthesizing style-preserving cartoons via non-negative style factorization

Zhang LIANG; Jun XIAO; Yue-ting ZHUANG

doi:10.1631/jzus.C1100202

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Synthesizing style-preserving cartoons via non-negative style factorization

计算机与自动化 | Updated：2022-12-09

- Synthesizing style-preserving cartoons via non-negative style factorization
- Synthesizing style-preserving cartoons via non-negative style factorization
- 信息与电子工程前沿（英文） 2012年13卷第3期页码：196-207
- Affiliations：
  
  Institute of Artificial Intelligence, School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
- Author bio：
  
  [ "Zhang LIANG, E-mail: liangzhang@zju.edu.cn" ]
  Jun XIAO, E-mail: junx@zju.edu.cn
  [ "Yue-ting ZHUANG, E-mail: yzhuang@zju.edu.cn" ]
- Funds：
  
  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)
- DOI：10.1631/jzus.C1100202
  中图分类号： TP391
- 收稿：2011-07-08，
  
  修回：2012-;2-;8，
  
  纸质出版：2012-03
- Accepted：
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Synthesizing style-preserving cartoons via non-negative style factorization[J]. 信息与电子工程前沿（英文）, 2012,13(3):196-207.

Zhang LIANG, Jun XIAO, Yue-ting ZHUANG. Synthesizing style-preserving cartoons via non-negative style factorization[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(3): 196-207.
Synthesizing style-preserving cartoons via non-negative style factorization[J]. 信息与电子工程前沿（英文）, 2012,13(3):196-207. DOI： 10.1631/jzus.C1100202.

Zhang LIANG, Jun XIAO, Yue-ting ZHUANG. Synthesizing style-preserving cartoons via non-negative style factorization[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(3): 196-207. DOI： 10.1631/jzus.C1100202.

摘要

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.

关键词

Keywords

references

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