Accurate and controllable image editing is a challenging task that has attracted significant
attention recently. Notably DragGAN developed by Pan et al.(2023) is an interactive point …

Synthesizing visual content that meets users' needs often requires flexible and precise
controllability of the pose, shape, expression, and layout of the generated objects. Existing …

Recent years have seen remarkable progress in deep learning powered visual content
creation. This includes deep generative 3D-aware image synthesis, which produces high …

We introduce a high resolution, 3D-consistent image and shape generation technique which
we call StyleSDF. Our method is trained on single view RGB data only, and stands on the …

Generative adversarial networks (GANs), a novel framework for training generative models
in an adversarial setup, have attracted significant attention in recent years. The two …

Denoising diffusion models are a powerful type of generative models used to capture
complex distributions of real-world signals. However, their applicability is limited to …

Abstract 3D-aware generative adversarial networks (GANs) synthesize high-fidelity and
multi-view-consistent facial images using only collections of single-view 2D imagery …

The inversion of real images into StyleGAN's latent space is a well-studied problem.
Nevertheless, applying existing approaches to real-world scenarios remains an open …

Recently, numerous facial editing techniques have been proposed that leverage the
generative power of a pretrained StyleGAN. To successfully edit an image this way, one …

Inspired by the ability of StyleGAN to generate highly re-alistic images in a variety of
domains, much recent work hasfocused on understanding how to use the latent spaces …