Abstract
Pre-learnt subspace methods, e.g., 3DMMs, are signifi-
cant exploration for the synthesis of 3D faces by assuming
that faces are in a linear class. However, the human face is
in a nonlinear manifold, and a new test are always not in
the pre-learnt subspace accurately because of the disparity
brought by ethnicity, age, gender, etc. In the paper, we propose a parametric T-spline morphable model (T-splineMM)
for 3D face representation, which has great advantages of
fitting data from an unknown source accurately. In the model, we describe a face by C2 T-spline surface, and divide
the face surface into several shape units (SUs), according
to facial action coding system (FACS), on T-mesh instead of
on the surface directly. A fitting algorithm is proposed to
optimize coefficients of T-spline control point components
along pre-learnt identity and expression subspaces, as well
as to optimize the details in refinement progress. As any
pre-learnt subspace is not complete to handle the variety
and details of faces and expressions, it covers a limited span of morphing. SUs division and detail refinement make
the model fitting the facial muscle deformation in a larger span of morphing subspace. We conduct experiments on
face scan data, kinect data as well as the space-time data to
test the performance of detail fitting, robustness to missing
data and noise, and to demonstrate the effectiveness of our
model. Convincing results are illustrated to demonstrate
the effectiveness of our model compared with the popular
methods