In this paper, we propose an end-to-end deep neural network model to generate high-quality 3D caricature with a simple face photo as input.
Given an input face photo, the goal of caricature generation is to produce stylized, exaggerated caricatures that share the same identity as the photo.
Caricature is an abstraction of a real person which distorts or exaggerates certain features, but still retains a likeness.
Deep convolutional neural networks (DCNNs) also create generalizable face representations, but with cascades of simulated neurons.
Furthermore, an attention mechanism is introduced to encourage our model to focus on the key facial parts so that more vivid details in these regions can be generated.
To construct the mapping between 2D sketches and a vertex-wise scaling field, a novel deep learning architecture is developed.
The key idea of our approach is to introduce an intrinsic deformation representation that has the capability of extrapolation, enabling us to create a deformation space from standard face datasets, which maintains face constraints and meanwhile is sufficiently large for producing exaggerated face models.