Abstract
We present a semi-parametric approach to photographic
image synthesis from semantic layouts. The approach combines the complementary strengths of parametric and nonparametric techniques. The nonparametric component is a
memory bank of image segments constructed from a training set of images. Given a novel semantic layout at test
time, the memory bank is used to retrieve photographic references that are provided as source material to a deep network. The synthesis is performed by a deep network that
draws on the provided photographic material. Experiments
on multiple semantic segmentation datasets show that the
presented approach yields considerably more realistic images than recent purely parametric techniques.