  Many methods have been proposed over the years to tackle the task of facial
3D geometry and texture recovery from a single image. Such methods often fail
to provide high-fidelity texture without relying on 3D facial scans during
training. In contrast, the complementary task of 3D facial generation has not
received as much attention. As opposed to the 2D texture domain, where GANs
have proven to produce highly realistic facial images, the more challenging 3D
geometry domain has not yet caught up to the same levels of realism and
diversity.
  In this paper, we propose a novel unified pipeline for both tasks, generation
of both geometry and texture, and recovery of high-fidelity texture. Our
texture model is learned, in an unsupervised fashion, from natural images as
opposed to scanned texture maps. To the best of our knowledge, this is the
first such unified framework independent of scanned textures.
  Our novel training pipeline incorporates a pre-trained 2D facial generator
coupled with a deep feature manipulation methodology. By applying precise 3DMM
fitting, we can seamlessly integrate our modeled textures into synthetically
generated background images forming a realistic composition of our textured
model with background, hair, teeth, and body. This enables us to apply transfer
learning from the domain of 2D image generation, thus, benefiting greatly from
the impressive results obtained in this domain.
  We provide a comprehensive study on several recent methods comparing our
model in generation and reconstruction tasks. As the extensive qualitative, as
well as quantitative analysis, demonstrate, we achieve state-of-the-art results
for both tasks.
