Data preparation. We first split 3DBiCar into a training
set (1,050 image-model pairs) and a testing set (450 pairs).
To support a stable training of BiCarNet, we next generate a
large number of synthetic paired data with the help of RaBit,
which are highly diversified in shape, posture, and texture.
To be specific, a lot of 3D textured models are first sampled
from the RaBit space, which are then rendered into images
from different camera views. This finally produce 13,650
pairs for training. Note that, BiCarNet takes an image with
foreground masked as input. All synthetic images naturally
have no background. For real images, all the foreground
masks are manually annotated.
RaBit- Parametric Modeling of 3D Biped Cartoon Characters with a Topological-consistent Dataset