The recent breakthroughs in natural language processing for model pretraining on large
quantities of data have opened the way for similar foundation models in computer vision …

Large-scale text-to-image generative models have been a revolutionary breakthrough in the
evolution of generative AI, synthesizing diverse images with highly complex visual concepts …

Finding correspondences between images is a fundamental problem in computer vision. In
this paper, we show that correspondence emerges in image diffusion models without any …

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 …

We present a method for zero-shot, text-driven editing of natural images and videos. Given
an image or a video and a text prompt, our goal is to edit the appearance of existing objects …

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 …

Despite the great success in 2D editing using user-friendly tools, such as Photoshop,
semantic strokes, or even text prompts, similar capabilities in 3D areas are still limited, either …

We present Neural Feature Fusion Fields (N3F),\a method that improves dense 2D image
feature extractors when the latter are applied to the analysis of multiple images …

Despite the rapid development of Neural Radiance Field (NeRF), the necessity of dense
covers largely prohibits its wider applications. While several recent works have attempted to …

Abstract Large-scale Text-to-Image (T2I) models have rapidly gained prominence across
creative fields generating visually compelling outputs from textual prompts. However …