  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. The results are shown in the supplementary
video at https://youtu.be/U4Q98lenGLQ
