  We propose an unsupervised segmentation framework for StyleGAN generated
objects. We build on two main observations. First, the features generated by
StyleGAN hold valuable information that can be utilized towards training
segmentation networks. Second, the foreground and background can often be
treated to be largely independent and be composited in different ways. For our
solution, we propose to augment the StyleGAN2 generator architecture with a
segmentation branch and to split the generator into a foreground and background
network. This enables us to generate soft segmentation masks for the foreground
object in an unsupervised fashion. On multiple object classes, we report
comparable results against state-of-the-art supervised segmentation networks,
while against the best unsupervised segmentation approach we demonstrate a
clear improvement, both in qualitative and quantitative metrics.
