Learning to Caricature via Semantic Shape Transform
This addresses the challenge of creating caricatures, which typically requires professional artistic skills, by providing an automated method for users to generate and manipulate such drawings.
The paper tackles the problem of automatically generating visually pleasing caricatures by proposing a semantic shape transform algorithm that predicts pixel-wise semantic correspondences and performs image warping to achieve dense shape transformation, resulting in diverse and plausible shape exaggerations while maintaining facial structures, as demonstrated on a large benchmark dataset with comparisons to state-of-the-art methods.
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.