  Caricature is an artistic representation that deliberately exaggerates the
distinctive features of a human face to convey humor or sarcasm. However,
reconstructing a 3D caricature from a 2D caricature image remains a challenging
task, mostly due to the lack of data. We propose to fill this gap by
introducing 3DCaricShop, the first large-scale 3D caricature dataset that
contains 2000 high-quality diversified 3D caricatures manually crafted by
professional artists. 3DCaricShop also provides rich annotations including a
paired 2D caricature image, camera parameters and 3D facial landmarks. To
demonstrate the advantage of 3DCaricShop, we present a novel baseline approach
for single-view 3D caricature reconstruction. To ensure a faithful
reconstruction with plausible face deformations, we propose to connect the good
ends of the detailrich implicit functions and the parametric mesh
representations. In particular, we first register a template mesh to the output
of the implicit generator and iteratively project the registration result onto
a pre-trained PCA space to resolve artifacts and self-intersections. To deal
with the large deformation during non-rigid registration, we propose a novel
view-collaborative graph convolution network (VCGCN) to extract key points from
the implicit mesh for accurate alignment. Our method is able to generate
highfidelity 3D caricature in a pre-defined mesh topology that is
animation-ready. Extensive experiments have been conducted on 3DCaricShop to
verify the significance of the database and the effectiveness of the proposed
method.
