  In this paper, we investigate the potential of image-to-image translation
(I2I) techniques for transferring realism to 3D-rendered facial images in the
context of Face Recognition (FR) systems. The primary motivation for using
3D-rendered facial images lies in their ability to circumvent the challenges
associated with collecting large real face datasets for training FR systems.
These images are generated entirely by 3D rendering engines, facilitating the
generation of synthetic identities. However, it has been observed that FR
systems trained on such synthetic datasets underperform when compared to those
trained on real datasets, on various FR benchmarks. In this work, we
demonstrate that by transferring the realism to 3D-rendered images (i.e.,
making the 3D-rendered images look more real), we can boost the performance of
FR systems trained on these more photorealistic images. This improvement is
evident when these systems are evaluated against FR benchmarks utilizing
real-world data, thereby paving new pathways for employing synthetic data in
real-world applications.
