itively manipulating shapes, expressions, and textures. Since
then, PCA-based parameterizing has gradually dominated
the area of statistical shape modeling over the past decades.
Following 3DMM, researchers model the whole head to
represent the neck region and 3D head rotations [12, 34].
Allen et al. [2] open up the study of full body parameteri-
zation. However, they focus only on body shape and omit
the body pose. SCAPE [3] represents body shape and pose
in terms of triangle deformations, while SMPL [37] mod-
els a whole range of natural shapes and poses based on
vertex displacements. SMPL-X [41] integrates SMPL [37]
with FLAME [34] head model and the MANO [48] hand
model for expressive capturing of bodies, hands and faces
together. With recent advances in deep learning, researchers
turn to explore nonlinear shape models using neural net-
works [1, 4, 8, 45, 56, 61]. However, since these non-linear
modeling methods are inferior in simplicity, robustness and
RaBit- Parametric Modeling of 3D Biped Cartoon Characters with a Topological-consistent Dataset