  In recent years, there has been significant progress in 2D generative face
models fueled by applications such as animation, synthetic data generation, and
digital avatars. However, due to the absence of 3D information, these 2D models
often struggle to accurately disentangle facial attributes like pose,
expression, and illumination, limiting their editing capabilities. To address
this limitation, this paper proposes a 3D controllable generative face model to
produce high-quality albedo and precise 3D shape leveraging existing 2D
generative models. By combining 2D face generative models with semantic face
manipulation, this method enables editing of detailed 3D rendered faces. The
proposed framework utilizes an alternating descent optimization approach over
shape and albedo. Differentiable rendering is used to train high-quality shapes
and albedo without 3D supervision. Moreover, this approach outperforms the
state-of-the-art (SOTA) methods in the well-known NoW benchmark for shape
reconstruction. It also outperforms the SOTA reconstruction models in
recovering rendered faces' identities across novel poses by an average of 10%.
Additionally, the paper demonstrates direct control of expressions in 3D faces
by exploiting latent space leading to text-based editing of 3D faces.
