  Analysis of faces is one of the core applications of computer vision, with
tasks ranging from landmark alignment, head pose estimation, expression
recognition, and face recognition among others. However, building reliable
methods requires time-consuming data collection and often even more
time-consuming manual annotation, which can be unreliable. In our work we
propose synthesizing such facial data, including ground truth annotations that
would be almost impossible to acquire through manual annotation at the
consistency and scale possible through use of synthetic data. We use a
parametric face model together with hand crafted assets which enable us to
generate training data with unprecedented quality and diversity (varying shape,
texture, expression, pose, lighting, and hair).
