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231224s2017 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2016.2628581
|2 doi
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|a eng
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| 100 |
1 |
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|a Yong Zhang
|e verfasserin
|4 aut
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| 245 |
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|a Data-Driven Synthesis of Cartoon Faces Using Different Styles
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|c 2017
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|a Text
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|a Date Revised 20.11.2019
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a 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
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| 650 |
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|a Journal Article
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| 700 |
1 |
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|a Weiming Dong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chongyang Ma
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Xing Mei
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ke Li
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Feiyue Huang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Bao-Gang Hu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Deussen, Oliver
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 26(2017), 1 vom: 15. Jan., Seite 464-478
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|g pages:464-478
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|u http://dx.doi.org/10.1109/TIP.2016.2628581
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