Abstract

Assisting people in efficiently producing visually plausi-
ble 3D characters has always been a fundamental research
topic in computer vision and computer graphics. Recent
learning-based approaches have achieved unprecedented
accuracy and efficiency in the area of 3D real human digiti-
zation. However, none of the prior works focus on modeling
3D biped cartoon characters, which are also in great demand
in gaming and filming. In this paper, we introduce 3DBiCar,
the first large-scale dataset of 3D biped cartoon characters,
and RaBit, the corresponding parametric model. Our dataset
contains 1,500 topologically consistent high-quality 3D tex-
tured models which are manually crafted by professional
artists. Built upon the data, RaBit is thus designed with a
SMPL-like linear blend shape model and a StyleGAN-based
neural UV-texture generator, simultaneously expressing the
shape, pose, and texture. To demonstrate the practicality
RaBit- Parametric Modeling of 3D Biped Cartoon Characters with a Topological-consistent Dataset