Abstract 3D GAN inversion aims to achieve high reconstruction fidelity and reasonable 3D
geometry simultaneously from a single image input. However, existing 3D GAN inversion …

We present a novel image inversion framework and a training pipeline to achieve high-
fidelity image inversion with high-quality attribute editing. Inverting real images into …

Recent progress in NeRF-based GANs has introduced a number of approaches for high-
resolution and high-fidelity generative modeling of human heads with a possibility for novel …

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 …

We propose a method that can generate cinemagraphs automatically from a still landscape
image using a pre-trained StyleGAN. Inspired by the success of recent unconditional video …

We present a generative adversarial network (GAN) inversion with high reconstruction and
editing quality. GAN inversion algorithms with expressive latent spaces produce near-perfect …

Abstract Model inversion (MI) attack reconstructs the private training data of a target model
given its output, posing a significant threat to deep learning models and data privacy. On …

GAN-based image attribute editing firstly leverages GAN Inversion to project real images
into the latent space of GAN and then manipulates corresponding latent codes. Recent …

In this paper, we present our framework for neural face/head reenactment whose goal is to
transfer the 3D head orientation and expression of a target face to a source face. Previous …

Abstract Generative Adversarial Networks (GANs) have revolutionized the field of image
synthesis by transforming randomly sampled latent codes into high-fidelity synthesized …