StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery

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...

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Vydané v:	Proceedings / IEEE International Conference on Computer Vision s. 2065 - 2074
Hlavní autori:	Patashnik, Or, Wu, Zongze, Shechtman, Eli, Cohen-Or, Daniel, Lischinski, Dani
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 01.10.2021
Predmet:	Computer vision Hair Image and video synthesis Image coding Manuals Neural generative models Optimization Vision + language Vision applications and systems Visualization
ISSN:	2380-7504
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