The breakthrough brought by generative adversarial networks (GANs) in computer vision
(CV) applications has gained a lot of attention in different fields due to their ability to capture …

Synthesizing larger texture images from a smaller exemplar is an important task in graphics
and vision. The conventional CNNs, recently adopted for synthesis, require to train and test …

One area of intense scientific interest for the study of sandstones, carbonates, and shale at
the pore scale is the use of limited image and petrophysical data to generate multiple …

Existing texture synthesis methods focus on generating large texture images given a small
texture sample. But such samples are typically assumed to be highly curated: rectangular …

We propose Neural Brushstroke Engine, the first method to apply deep generative models to
learn a distribution of interactive drawing tools. Our conditional GAN model learns the latent …

We present a novel texture synthesis framework, enabling the generation of infinite, high-
quality 3D textures given a 2D exemplar image. Inspired by recent advances in natural …

We present a technique that leverages 2D generative diffusion models (DMs) for interactive
texture painting on the surface of 3D meshes. Unlike existing texture painting systems, our …

Conventional CNNs for texture synthesis consist of a sequence of (de)-convolution and
up/down-sampling layers, where each layer operates locally and lacks the ability to capture …

The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed
proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel …

Motion blur is a common problem in optical imaging, which is caused by the relative
displacement between the subject and the camera in the exposure process of the camera …