  The last few years have witnessed the great success of non-linear generative
models in synthesizing high-quality photorealistic face images. Many recent 3D
facial texture reconstruction and pose manipulation from a single image
approaches still rely on large and clean face datasets to train image-to-image
Generative Adversarial Networks (GANs). Yet the collection of such a large
scale high-resolution 3D texture dataset is still very costly and difficult to
maintain age/ethnicity balance. Moreover, regression-based approaches suffer
from generalization to the in-the-wild conditions and are unable to fine-tune
to a target-image. In this work, we propose an unsupervised approach for
one-shot 3D facial texture completion that does not require large-scale texture
datasets, but rather harnesses the knowledge stored in 2D face generators. The
proposed approach rotates an input image in 3D and fill-in the unseen regions
by reconstructing the rotated image in a 2D face generator, based on the
visible parts. Finally, we stitch the most visible textures at different angles
in the UV image-plane. Further, we frontalize the target image by projecting
the completed texture into the generator. The qualitative and quantitative
experiments demonstrate that the completed UV textures and frontalized images
are of high quality, resembles the original identity, can be used to train a
texture GAN model for 3DMM fitting and improve pose-invariant face recognition.
