  Image-to-image translation (i2i) networks suffer from entanglement effects in
presence of physics-related phenomena in target domain (such as occlusions,
fog, etc), lowering altogether the translation quality, controllability and
variability. In this paper, we propose a general framework to disentangle
visual traits in target images. Primarily, we build upon collection of simple
physics models, guiding the disentanglement with a physical model that renders
some of the target traits, and learning the remaining ones. Because physics
allows explicit and interpretable outputs, our physical models (optimally
regressed on target) allows generating unseen scenarios in a controllable
manner. Secondarily, we show the versatility of our framework to neural-guided
disentanglement where a generative network is used in place of a physical model
in case the latter is not directly accessible. Altogether, we introduce three
strategies of disentanglement being guided from either a fully differentiable
physics model, a (partially) non-differentiable physics model, or a neural
network. The results show our disentanglement strategies dramatically increase
performances qualitatively and quantitatively in several challenging scenarios
for image translation.
