Spatially-Adaptive Multilayer Selection for GAN Inversion and Editing. (arXiv:2206.08357v1 [cs.CV])

Web: http://arxiv.org/abs/2206.08357

Existing GAN inversion and editing methods work well for aligned objects with
a clean background, such as portraits and animal faces, but often struggle for
more difficult categories with complex scene layouts and object occlusions,
such as cars, animals, and outdoor images. We propose a new method to invert
and edit such complex images in the latent space of GANs, such as StyleGAN2.
Our key idea is to explore inversion with a collection of layers, spatially
adapting the inversion process …

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