GAN inversion aims to invert a given image back into the latent space of a pretrained GAN
model so that the image can be faithfully reconstructed from the inverted code by the …

Recent work has shown that a variety of semantics emerge in the latent space of Generative
Adversarial Networks (GANs) when being trained to synthesize images. However, it is …

Real-world image manipulation has achieved fantastic progress in recent years as a result
of the exploration and utilization of GAN latent spaces. GAN inversion is the first step in this …

GAN inversion and editing via StyleGAN maps an input image into the embedding spaces
(W, W^+, and F) to simultaneously maintain image fidelity and meaningful manipulation …

Existing GAN inversion methods are stuck in a paradox that the inverted codes can either
achieve high-fidelity reconstruction, or retain the editing capability. Having only one of them …

Existing GAN inversion methods fail to provide codes for reliable reconstruction and flexible
editing simultaneously. This paper presents a transformer-based image inversion and …

Abstract Inverting a Generative Adversarial Network (GAN) facilitates a wide range of image
editing tasks using pre-trained generators. Existing methods typically employ the latent …

The latent space of a Generative Adversarial Network (GAN) has been shown to encode rich
semantics within some subspaces. To identify these subspaces, researchers typically …

For successful semantic editing of real images, it is critical for a GAN inversion method to
find an in-domain latent code that aligns with the domain of a pre-trained GAN model …

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 …