Large-scale text-to-image generative models have shown their remarkable ability to
synthesize diverse, high-quality images. However, directly applying these models for real …

Text-to-image personalization aims to teach a pre-trained diffusion model to reason about
novel, user provided concepts, embedding them into new scenes guided by natural …

Recently, GAN inversion methods combined with Contrastive Language-Image Pretraining
(CLIP) enables zero-shot image manipulation guided by text prompts. However, their …

With the massive proliferation of data-driven algorithms, such as deep learning-based
approaches, the availability of high-quality data is of great interest. Volumetric data is very …

Guided image synthesis enables everyday users to create and edit photo-realistic images
with minimum effort. The key challenge is balancing faithfulness to the user input (eg, hand …

Diffusion probabilistic models (DPMs) have achieved remarkable quality in image
generation that rivals GANs'. But unlike GANs, DPMs use a set of latent variables that lack …

Generative adversarial networks (GANs) have drawn enormous attention due to their simple
yet effective training mechanism and superior image generation quality. With the ability to …

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 …

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 …