论文标题

通过语义形状转换学习到漫画

Learning to Caricature via Semantic Shape Transform

论文作者

Chu, Wenqing, Hung, Wei-Chih, Tsai, Yi-Hsuan, Chang, Yu-Ting, Li, Yijun, Cai, Deng, Yang, Ming-Hsuan

论文摘要

漫画是一种艺术绘画,以抽象或夸大人的面部特征而创建。渲染视觉上令人愉悦的漫画是一项艰巨的任务,需要专业技能,因此,设计一种自动生成此类图纸的方法引起了极大的兴趣。为了应对大型变化,我们提出了一种基于语义形状变换的算法,以产生多种多样的形状夸张。具体而言,我们预测了像素的语义对应关系,并在输入照片上执行图像扭曲以实现致密形状转换。我们表明,所提出的框架能够在保持面部结构的同时使视觉上令人愉悦的形状夸张。此外,我们的模型允许用户通过语义图来操纵形状。我们证明了我们的方法对与最新方法的大型照片基准数据集的有效性。

Caricature is an artistic drawing created to abstract or exaggerate facial features of a person. Rendering visually pleasing caricatures is a difficult task that requires professional skills, and thus it is of great interest to design a method to automatically generate such drawings. To deal with large shape changes, we propose an algorithm based on a semantic shape transform to produce diverse and plausible shape exaggerations. Specifically, we predict pixel-wise semantic correspondences and perform image warping on the input photo to achieve dense shape transformation. We show that the proposed framework is able to render visually pleasing shape exaggerations while maintaining their facial structures. In addition, our model allows users to manipulate the shape via the semantic map. We demonstrate the effectiveness of our approach on a large photograph-caricature benchmark dataset with comparisons to the state-of-the-art methods.

扫码加入交流群

加入微信交流群

微信交流群二维码

扫码加入学术交流群,获取更多资源