  A 3D caricature is an exaggerated 3D depiction of a human face. The goal of
this paper is to model the variations of 3D caricatures in a compact parameter
space so that we can provide a useful data-driven toolkit for handling 3D
caricature deformations. To achieve the goal, we propose an MLP-based framework
for building a deformable surface model, which takes a latent code and produces
a 3D surface. In the framework, a SIREN MLP models a function that takes a 3D
position on a fixed template surface and returns a 3D displacement vector for
the input position. We create variations of 3D surfaces by learning a
hypernetwork that takes a latent code and produces the parameters of the MLP.
Once learned, our deformable model provides a nice editing space for 3D
caricatures, supporting label-based semantic editing and point-handle-based
deformation, both of which produce highly exaggerated and natural 3D caricature
shapes. We also demonstrate other applications of our deformable model, such as
automatic 3D caricature creation.
