CLIP2StyleGAN: Unsupervised Extraction of StyleGAN Edit Directions

Understand

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.