StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery

31 Mar 2021 • Or Patashnik • Zongze Wu • Eli Shechtman • Daniel Cohen-Or • Dani Lischinski

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... (read more)

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METHOD	TYPE
Leaky ReLU	Activation Functions
R1 Regularization	Regularization
Adaptive Instance Normalization	Normalization
Dense Connections	Feedforward Networks
Convolution	Convolutions
Feedforward Network	Feedforward Networks
StyleGAN	Generative Models

METHOD

TYPE

Leaky ReLU