  We propose spatially-adaptive normalization, a simple but effective layer for
synthesizing photorealistic images given an input semantic layout. Previous
methods directly feed the semantic layout as input to the deep network, which
is then processed through stacks of convolution, normalization, and
nonlinearity layers. We show that this is suboptimal as the normalization
layers tend to ``wash away'' semantic information. To address the issue, we
propose using the input layout for modulating the activations in normalization
layers through a spatially-adaptive, learned transformation. Experiments on
several challenging datasets demonstrate the advantage of the proposed method
over existing approaches, regarding both visual fidelity and alignment with
input layouts. Finally, our model allows user control over both semantic and
style. Code is available at https://github.com/NVlabs/SPADE .
