Based on 3DBiCar, we further propose a generative
model, dubbed RaBit, for 3D biped cartoon character genera-
tion. It combines a linear blend shape model with a neural tex-
ture generator and simultaneously parameterizes the shape,
pose, and texture to a low-dimensional parametric space. For
shape and pose modeling, numerous methods have shown
principal component analysis’s (PCA’s) advantage in build-
ing decent statistical shape models [5, 12, 34, 41]. Inspired
by SMPL [37], we utilize the traditional PCA technique to
parameterize shape. Due to the variety and complexity of
cartoon texture, directly adopting PCA for texture model-
ing fails to reconstruct details and falls into blurry results.
We tackle this problem by introducing a StyleGAN-based
generator.

To explore the practical usage of 3DBiCar and RaBit, we
RaBit- Parametric Modeling of 3D Biped Cartoon Characters with a Topological-consistent Dataset