StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery
StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery (ICCV 2021 Oral)
StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery
Or Patashnik*, Zongze Wu*, Eli Shechtman, Daniel Cohen-Or, Dani Lischinski
*Equal contribution, ordered alphabetically
https://arxiv.org/abs/2103.17249
Abstract: Inspired by the ability of StyleGAN to generate highly realistic images in a variety of domains, much recent work has focused on understanding how to use the latent spaces of StyleGAN to manipulate generated and real images. However, discovering semantically meaningful latent manipulations typically involves painstaking human examination of the many degrees of freedom, or an annotated collection of images for each desired manipulation. In this work, we explore leveraging the power of recently introduced Contrastive Language-Image Pre-training (CLIP) models in order to develop a