Diffusion models have emerged as a powerful new family of deep generative models with
record-breaking performance in many applications, including image synthesis, video …

The performance of face analysis and recognition systems depends on the quality of the
acquired face data, which is influenced by numerous factors. Automatically assessing the …

Text-to-image models offer unprecedented freedom to guide creation through natural
language. Yet, it is unclear how such freedom can be exercised to generate images of …

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 …

Score-based generative models (SGMs) have recently demonstrated impressive results in
terms of both sample quality and distribution coverage. However, they are usually applied …

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 …

Diffusion models learn to restore noisy data, which is corrupted with different levels of noise,
by optimizing the weighted sum of the corresponding loss terms, ie, denoising score …

Text-to-image model personalization aims to introduce a user-provided concept to the
model, allowing its synthesis in diverse contexts. However, current methods primarily focus …

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 …