Learning to Caricature via Semantic Shape Transform

• URL: http://arxiv.org/abs/2008.05090v2
• Date: Thu, 13 Aug 2020 06:58:02 GMT
• Title: Learning to Caricature via Semantic Shape Transform
• Authors: Wenqing Chu, Wei-Chih Hung, Yi-Hsuan Tsai, Yu-Ting Chang, Yijun Li, Deng Cai, Ming-Hsuan Yang
• Abstract summary: We propose an algorithm based on a semantic shape transform to produce shape exaggerations. We show that the proposed framework is able to render visually pleasing shape exaggerations while maintaining their facial structures.
• Score: 95.25116681761142
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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