Data-driven face cartoon stylization

Yong Zhang, Weiming Dong, O. Deussen, Feiyue Huang, Kexin Li, Bao-Gang Hu
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引用次数: 20

Abstract

This paper presents a data-driven framework for generating cartoon-like facial representations from a given portrait image. We solve our problem by an optimization that simultaneously considers a desired artistic style, image-cartoon relationships of facial components as well as automatic adjustment of the image composition. The stylization operation consists of two steps: a face parsing step to localize and extract facial components from the input image; a cartoon generation step to cartoonize the face according to the extracted information. The components of the cartoon are assembled from a database of stylized facial components. Quantifying the similarity between facial components of input and cartoon is done by image feature matching. We incorporate prior knowledge about photo-cartoon relationships and the optimal composition of cartoon facial components extracted from a set of cartoon faces to maintain a natural and attractive look of the results.
数据驱动的面部卡通风格
本文提出了一个数据驱动的框架,用于从给定的肖像图像中生成类似卡通的面部表征。我们通过同时考虑所需的艺术风格,面部成分的图像-卡通关系以及图像组成的自动调整的优化来解决我们的问题。风格化操作包括两个步骤:人脸解析步骤,从输入图像中定位和提取人脸成分;卡通生成步骤,根据提取的信息对人脸进行卡通化。漫画的组成部分是从一个程式化的面部组成部分的数据库中组装的。通过图像特征匹配来量化输入图像与卡通图像的面部成分之间的相似度。我们结合了关于照片-卡通关系的先验知识和从一组卡通面孔中提取的卡通面部成分的最佳组成,以保持结果的自然和吸引人的外观。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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