  We present VoloGAN, an adversarial domain adaptation network that translates
synthetic RGB-D images of a high-quality 3D model of a person, into RGB-D
images that could be generated with a consumer depth sensor. This system is
especially useful to generate high amount training data for single-view 3D
reconstruction algorithms replicating the real-world capture conditions, being
able to imitate the style of different sensor types, for the same high-end 3D
model database. The network uses a CycleGAN framework with a U-Net architecture
for the generator and a discriminator inspired by SIV-GAN. We use different
optimizers and learning rate schedules to train the generator and the
discriminator. We further construct a loss function that considers image
channels individually and, among other metrics, evaluates the structural
similarity. We demonstrate that CycleGANs can be used to apply adversarial
domain adaptation of synthetic 3D data to train a volumetric video generator
model having only few training samples.
