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.
|Number of pages||17|
|Journal||International Journal of Computer Vision|
|Publication status||Published - 2021 Sept|
Bibliographical noteFunding Information:
This work was supported in part by The National Key Research and Development Program of China (Grant Nos: 2018AAA0101400), in part by The National Nature Science Foundation of China (Grant Nos: 62036009, 61936006), in part by Innovation Capability Support Program of Shaanxi (Program No. 2021TD-05). W.-C. Hung, Y.-T. Chang, Y. Li, and M.-H. Yang were supported in part by National Science Foundation CAREER Grant 1149783.
© 2021, The Author(s).
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
- Artificial Intelligence