  The task of transforming a furnished room image into a background-only is
extremely challenging since it requires making large changes regarding the
scene context while still preserving the overall layout and style. In order to
acquire photo-realistic and structural consistent background, existing deep
learning methods either employ image inpainting approaches or incorporate the
learning of the scene layout as an individual task and leverage it later in a
not fully differentiable semantic region-adaptive normalization module. To
tackle these drawbacks, we treat scene layout generation as a feature linear
transformation problem and propose a simple yet effective adjusted fully
differentiable soft semantic region-adaptive normalization module (softSEAN)
block. We showcase the applicability in diminished reality and depth estimation
tasks, where our approach besides the advantages of mitigating training
complexity and non-differentiability issues, surpasses the compared methods
both quantitatively and qualitatively. Our softSEAN block can be used as a
drop-in module for existing discriminative and generative models.
Implementation is available on vcl3d.github.io/PanoDR/.
