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Data-Driven Synthesis of Cartoon Faces Using Different Styles
Jul 24, 2017Author:
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Title: Data-Driven Synthesis of Cartoon Faces Using Different Styles

 Authors: Zhang, Y; Dong, WM; Ma, CY; Mei, X; Li, K; Huang, FY; Hu, BG; Deussen, O

 Author Full Names: Zhang, Yong; Dong, Weiming; Ma, Chongyang; Mei, Xing; Li, Ke; Huang, Feiyue; Hu, Bao-Gang; Deussen, Oliver

 Source: IEEE TRANSACTIONS ON IMAGE PROCESSING, 26 (1):464-478; 10.1109/TIP.2016.2628581 JAN 2017

Language: English

 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.

 ISSN: 1057-7149

 eISSN: 1941-0042

 IDS Number: EP2OB

 Unique ID: WOS:000397221700010

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