  Many modern online 3D applications and video games rely on parametric models
of human faces for creating believable avatars. However, manually reproducing
someone's facial likeness with a parametric model is difficult and
time-consuming. Machine Learning solution for that task is highly desirable but
is also challenging. The paper proposes a novel approach to the so-called
Face-to-Parameters problem (F2P for short), aiming to reconstruct a parametric
face from a single image. The proposed method utilizes synthetic data, domain
decomposition, and domain adaptation to address multifaceted challenges in
solving the F2P. The open-sourced codebase illustrates our key observations and
provides means for quantitative evaluation. The presented approach proves
practical in an industrial application; it improves accuracy and allows for
more efficient models training. The techniques have the potential to extend to
other types of parametric models.
