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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Bibliographic Details

Published in:	Proceedings / IEEE International Conference on Computer Vision pp. 2065 - 2074
Main Authors:	Patashnik, Or, Wu, Zongze, Shechtman, Eli, Cohen-Or, Daniel, Lischinski, Dani
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.10.2021
Subjects:	Computer vision Hair Image and video synthesis Image coding Manuals Neural generative models Optimization Vision + language Vision applications and systems Visualization
ISSN:	2380-7504
Online Access:	Get full text
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