Caricature is a pictorial representation or description that deliberately exaggerates a person’s distinctive features or peculiarities to create an easily identifiable visual likeness with a comic effect. This vivid art form contains the concepts of abstraction, simplification and exaggeration.
Source: Alive Caricature from 2D to 3D
We introduce a saliency-based distortion layer for convolutional neural networks that helps to improve the spatial sampling of input data for a given task.
To this end, we first build a dataset with various styles of 2D caricatures and their corresponding 3D shapes, and then build a parametric model on vertex based deformation space for 3D caricature face.
However, current state-of-the-art face parsing methods require large amounts of labeled data on the pixel-level and such process for caricature is tedious and labor-intensive.
Further, recognizing the identity in the image by knowledge transfer using a combination of shared and modality specific representations, resulted in an unprecedented performance of 85% rank-1 accuracy for caricatures and 95% rank-1 accuracy for visual images.
Facial caricature is an art form of drawing faces in an exaggerated way to convey humor or sarcasm.
We present an approach for learning to translate faces in the wild from the source photo domain to the target caricature domain with different styles, which can also be used for other high-level image-to-image translation tasks.
The implementation of the proposed method is available at https://github. com/KeleiHe/DAAN.