Text-to-image models offer a new level of creative flexibility by allowing users to guide the
image generation process through natural language. However, using these models to …

We present a method for semantically transferring the visual appearance of one natural
image to another. Specifically, our goal is to generate an image in which objects in a source …

Recent advancements in text-to-image generative models have demonstrated a remarkable
ability to capture a deep semantic understanding of images. In this work, we leverage this …

The generative adversarial network (GAN) framework has emerged as a powerful tool for
various image and video synthesis tasks, allowing the synthesis of visual content in an …

Recently there has been an interest in the potential of learning generative models from a
single image, as opposed to from a large dataset. This task is of significance, as it means …

Image manipulation dates back long before the deep learning era. The classical prevailing
approaches were based on maximizing patch similarity between the input and generated …

We present a method for semantically transferring the visual appearance of one natural
image to another. Specifically, our goal is to generate an image in which objects in a source …

Real-time graphics applications require high-quality textured materials to convey realism in
virtual environments. Generating these textures is challenging as they need to be visually …

GANs are able to perform generation and manipulation tasks, trained on a single video.
However, these single video GANs require unreasonable amount of time to train on a single …