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 car...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.展开更多
You know you’re in a fun palace when the subway elevator walls are decorated with plastic cutouts of cartoon characters loved the world over.There is also something electric in the energy of a crowd that rushes in ch...You know you’re in a fun palace when the subway elevator walls are decorated with plastic cutouts of cartoon characters loved the world over.There is also something electric in the energy of a crowd that rushes in childlike wonder to the entrance of expectation.展开更多
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)
文摘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.
文摘You know you’re in a fun palace when the subway elevator walls are decorated with plastic cutouts of cartoon characters loved the world over.There is also something electric in the energy of a crowd that rushes in childlike wonder to the entrance of expectation.
