  Improvements in data-capture and face modeling techniques have enabled us to
create high-fidelity realistic face models. However, driving these realistic
face models requires special input data, e.g. 3D meshes and unwrapped textures.
Also, these face models expect clean input data taken under controlled lab
environments, which is very different from data collected in the wild. All
these constraints make it challenging to use the high-fidelity models in
tracking for commodity cameras. In this paper, we propose a self-supervised
domain adaptation approach to enable the animation of high-fidelity face models
from a commodity camera. Our approach first circumvents the requirement for
special input data by training a new network that can directly drive a face
model just from a single 2D image. Then, we overcome the domain mismatch
between lab and uncontrolled environments by performing self-supervised domain
adaptation based on "consecutive frame texture consistency" based on the
assumption that the appearance of the face is consistent over consecutive
frames, avoiding the necessity of modeling the new environment such as lighting
or background. Experiments show that we are able to drive a high-fidelity face
model to perform complex facial motion from a cellphone camera without
requiring any labeled data from the new domain.
