  The success of StyleGAN has enabled unprecedented semantic editing
capabilities, on both synthesized and real images. However, such editing
operations are either trained with semantic supervision or described using
human guidance. In another development, the CLIP architecture has been trained
with internet-scale image and text pairings and has been shown to be useful in
several zero-shot learning settings. In this work, we investigate how to
effectively link the pretrained latent spaces of StyleGAN and CLIP, which in
turn allows us to automatically extract semantically labeled edit directions
from StyleGAN, finding and naming meaningful edit operations without any
additional human guidance. Technically, we propose two novel building blocks;
one for finding interesting CLIP directions and one for labeling arbitrary
directions in CLIP latent space. The setup does not assume any pre-determined
labels and hence we do not require any additional supervised text/attributes to
build the editing framework. We evaluate the effectiveness of the proposed
method and demonstrate that extraction of disentangled labeled StyleGAN edit
directions is indeed possible, and reveals interesting and non-trivial edit
directions.
