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 …

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 …

Dataset Distillation aims to distill an entire dataset's knowledge into a few synthetic images.
The idea is to synthesize a small number of synthetic data points that, when given to a …

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 …

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

Recently, there has been a surge of diverse methods for performing image editing by
employing pre-trained unconditional generators. Applying these methods on real images …

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 …

We propose Neural Actor (NA), a new method for high-quality synthesis of humans from
arbitrary viewpoints and under arbitrary controllable poses. Our method is developed upon …

Recently, the power of unconditional image synthesis has significantly advanced through
the use of Generative Adversarial Networks (GANs). The task of inverting an image into its …