  Image-to-image translation is affected by entanglement phenomena, which may
occur in case of target data encompassing occlusions such as raindrops, dirt,
etc. Our unsupervised model-based learning disentangles scene and occlusions,
while benefiting from an adversarial pipeline to regress physical parameters of
the occlusion model. The experiments demonstrate our method is able to handle
varying types of occlusions and generate highly realistic translations,
qualitatively and quantitatively outperforming the state-of-the-art on multiple
datasets.
