Enjoy Your Editing: Controllable GANs for Image Editing via Latent Space
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- URL: http://arxiv.org/abs/2102.01187v2
- Date: Wed, 3 Feb 2021 07:21:18 GMT
- Title: Enjoy Your Editing: Controllable GANs for Image Editing via Latent Space
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- Authors: Peiye Zhuang, Oluwasanmi Koyejo, Alexander G. Schwing
- Abstract summary: Controllable semantic image editing enables a user to change entire image attributes with few clicks.
Current approaches often suffer from attribute edits that are entangled, global image identity changes, and diminished photo-realism.
We propose quantitative evaluation strategies for measuring controllable editing performance, unlike prior work which primarily focuses on qualitative evaluation.
- Score: 136.53288628437355
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Controllable semantic image editing enables a user to change entire image
attributes with few clicks, e.g., gradually making a summer scene look like it
was taken in winter. Classic approaches for this task use a Generative
Adversarial Net (GAN) to learn a latent space and suitable latent-space
transformations. However, current approaches often suffer from attribute edits
that are entangled, global image identity changes, and diminished
photo-realism. To address these concerns, we learn multiple attribute
transformations simultaneously, we integrate attribute regression into the
training of transformation functions, apply a content loss and an adversarial
loss that encourage the maintenance of image identity and photo-realism. We
propose quantitative evaluation strategies for measuring controllable editing
performance, unlike prior work which primarily focuses on qualitative
evaluation. Our model permits better control for both single- and
multiple-attribute editing, while also preserving image identity and realism
during transformation. We provide empirical results for both real and synthetic
images, highlighting that our model achieves state-of-the-art performance for
targeted image manipulation.
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