  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 single images. With the advent of new 3D sensors, many 3D
facial 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 optimize with regards to the illumination parameters.
Furthermore, we propose a new fast algorithm for fitting the 3DMM in arbitrary
images. Finally, we have captured the first 3D facial database with relatively
unconstrained conditions and report quantitative evaluations with
state-of-the-art performance. Complementary qualitative reconstruction results
are demonstrated on standard "in-the-wild" facial databases. An open source
implementation of our technique is released as part of the Menpo Project.
