Semantic Image Synthesis with Unconditional Generator
Jungwoo Chae Hyunin Cho Sooyeon Go Kyungmook Choi Youngjung Uh
Semantic image synthesis (SIS) aims to generate realistic images that match
given semantic masks. Despite recent advances allowing high-quality results and
precise spatial control, they require a massive semantic segmentation dataset
for training the models. Instead, we propose to employ a pre-trained
unconditional generator and rearrange its feature maps according to proxy
masks. The proxy masks are prepared from the feature maps of random samples in
the generator by simple clustering. The feature rearranger learns to rearrange
original feature maps to match the shape of the proxy masks that are either
from the original sample itself or from random samples. Then we introduce a
semantic mapper that produces the proxy masks from various input conditions
including semantic masks. Our method is versatile across various applications
such as free-form spatial editing of real images, sketch-to-photo, and even
scribble-to-photo. Experiments validate advantages of our method on a range of
datasets: human faces, animal faces, and buildings.