Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/233553
Title: Data-Driven Synthesis of Cartoon Faces Using Different Styles
Authors: Yong Zhang;Weiming Dong;Chongyang Ma;Xing Mei;Ke Li;Feiyue Huang;Bao-Gang Hu;Oliver Deussen
Year: 2017
Publisher: IEEE
Abstract: This paper presents a data-driven approach for automatically generating cartoon faces in different styles from a given portrait image. Our stylization pipeline consists of two steps: an offline analysis step to learn about how to select and compose facial components from the databases; a runtime synthesis step to generate the cartoon face by assembling parts from a database of stylized facial components. We propose an optimization framework that, for a given artistic style, simultaneously considers the desired image-cartoon relationships of the facial components and a proper adjustment of the image composition. We measure the similarity between facial components of the input image and our cartoon database via image feature matching, and introduce a probabilistic framework for modeling the relationships between cartoon facial components. We incorporate prior knowledge about image-cartoon relationships and the optimal composition of facial components extracted from a set of cartoon faces to maintain a natural, consistent, and attractive look of the results. We demonstrate generality and robustness of our approach by applying it to a variety of portrait images and compare our output with stylized results created by artists via a comprehensive user study.
URI: http://localhost/handle/Hannan/233553
volume: 26
issue: 1
More Information: 464,
478
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7742963.pdf6.45 MBAdobe PDF
Title: Data-Driven Synthesis of Cartoon Faces Using Different Styles
Authors: Yong Zhang;Weiming Dong;Chongyang Ma;Xing Mei;Ke Li;Feiyue Huang;Bao-Gang Hu;Oliver Deussen
Year: 2017
Publisher: IEEE
Abstract: This paper presents a data-driven approach for automatically generating cartoon faces in different styles from a given portrait image. Our stylization pipeline consists of two steps: an offline analysis step to learn about how to select and compose facial components from the databases; a runtime synthesis step to generate the cartoon face by assembling parts from a database of stylized facial components. We propose an optimization framework that, for a given artistic style, simultaneously considers the desired image-cartoon relationships of the facial components and a proper adjustment of the image composition. We measure the similarity between facial components of the input image and our cartoon database via image feature matching, and introduce a probabilistic framework for modeling the relationships between cartoon facial components. We incorporate prior knowledge about image-cartoon relationships and the optimal composition of facial components extracted from a set of cartoon faces to maintain a natural, consistent, and attractive look of the results. We demonstrate generality and robustness of our approach by applying it to a variety of portrait images and compare our output with stylized results created by artists via a comprehensive user study.
URI: http://localhost/handle/Hannan/233553
volume: 26
issue: 1
More Information: 464,
478
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7742963.pdf6.45 MBAdobe PDF
Title: Data-Driven Synthesis of Cartoon Faces Using Different Styles
Authors: Yong Zhang;Weiming Dong;Chongyang Ma;Xing Mei;Ke Li;Feiyue Huang;Bao-Gang Hu;Oliver Deussen
Year: 2017
Publisher: IEEE
Abstract: This paper presents a data-driven approach for automatically generating cartoon faces in different styles from a given portrait image. Our stylization pipeline consists of two steps: an offline analysis step to learn about how to select and compose facial components from the databases; a runtime synthesis step to generate the cartoon face by assembling parts from a database of stylized facial components. We propose an optimization framework that, for a given artistic style, simultaneously considers the desired image-cartoon relationships of the facial components and a proper adjustment of the image composition. We measure the similarity between facial components of the input image and our cartoon database via image feature matching, and introduce a probabilistic framework for modeling the relationships between cartoon facial components. We incorporate prior knowledge about image-cartoon relationships and the optimal composition of facial components extracted from a set of cartoon faces to maintain a natural, consistent, and attractive look of the results. We demonstrate generality and robustness of our approach by applying it to a variety of portrait images and compare our output with stylized results created by artists via a comprehensive user study.
URI: http://localhost/handle/Hannan/233553
volume: 26
issue: 1
More Information: 464,
478
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7742963.pdf6.45 MBAdobe PDF