3D Morphable Models (3DMMs) are powerful statistical
models of 3D facial shape and texture, and among the state-
of-the-art methods for reconstructing facial shape from sin-
gle images. With the advent of new 3D sensors, many 3D fa-
cial datasets have been collected containing both neutral as
well as expressive faces. However, all datasets are captured
under controlled conditions. Thus, even though powerful
3D facial shape models can be learnt from such data, it is
difficult to build statistical texture models that are sufficient
to reconstruct faces captured in unconstrained conditions
(“in-the-wild”). In this paper, we propose the first, to the
best of our knowledge, “in-the-wild” 3DMM by combining
a powerful statistical model of facial shape, which describes
both identity and expression, with an “in-the-wild” texture
model. We show that the employment of such an “in-the-
wild” texture model greatly simplifies the fitting procedure,
because there is no need to optimise with regards to the illu-
mination parameters. Furthermore, we propose a new fast
algorithm for fitting the 3DMM in arbitrary images. Fi-
nally, we have captured the first 3D facial database with
relatively unconstrained conditions and report quantitative
evaluations with state-of-the-art performance. Complemen-
tary qualitative reconstruction results are demonstrated on
standard “in-the-wild” facial databases.