  This paper tackles a challenging problem of generating photorealistic images
from semantic layouts in few-shot scenarios where annotated training pairs are
hardly available but pixel-wise annotation is quite costly. We present a
training strategy that performs pseudo labeling of semantic masks using the
StyleGAN prior. Our key idea is to construct a simple mapping between the
StyleGAN feature and each semantic class from a few examples of semantic masks.
With such mappings, we can generate an unlimited number of pseudo semantic
masks from random noise to train an encoder for controlling a pre-trained
StyleGAN generator. Although the pseudo semantic masks might be too coarse for
previous approaches that require pixel-aligned masks, our framework can
synthesize high-quality images from not only dense semantic masks but also
sparse inputs such as landmarks and scribbles. Qualitative and quantitative
results with various datasets demonstrate improvement over previous approaches
with respect to layout fidelity and visual quality in as few as one- or
five-shot settings.
