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Caricature is an artistic representation that deliberately exaggerates thedistinctive features of a human face to convey humor or sarcasm. However,reconstructing a 3D caricature from a 2D caricature image remains a challengingtask, mostly due to the lack of data. We propose to fill this gap byintroducing 3DCaricShop, the first large-scale 3D caricature dataset thatcontains 2000 high-quality diversified 3D caricatures manually crafted byprofessional artists. 3DCaricShop also provides rich annotations including apaired 2D caricature image, camera parameters and 3D facial landmarks. Todemonstrate the advantage of 3DCaricShop, we present a novel baseline approachfor single-view 3D caricature reconstruction. To ensure a faithfulreconstruction with plausible face deformations, we propose to connect the goodends of the detailrich implicit functions and the parametric meshrepresentations. In particular, we first register a template mesh to the outputof the implicit generator and iteratively project the registration result ontoa pre-trained PCA space to resolve artifacts and self-intersections. To dealwith the large deformation during non-rigid registration, we propose a novelview-collaborative graph convolution network (VCGCN) to extract key points fromthe implicit mesh for accurate alignment. Our method is able to generatehighfidelity 3D caricature in a pre-defined mesh topology that isanimation-ready. Extensive experiments have been conducted on 3DCaricShop toverify the significance of the database and the effectiveness of the proposedmethod.

3DCaricShop: A Dataset and A Baseline Method for Single-view 3D Caricature Face Reconstruction