Recent years have witnessed significant progress in example-based texture synthesis
algorithms. Given an example texture, these methods produce a larger texture that is tailored …

Abstract Neural Style Transfer has shown very exciting results enabling new forms of image
manipulation. Here we extend the existing method to introduce control over spatial location …

Abstract Multiple-Point Simulations (MPS) is a family of geostatistical tools that has received
a lot of attention in recent years for the characterization of spatial phenomena in …

Modern applications and progress in deep learning research have created renewed interest
for generative models of text and of images. However, even today it is unclear what objective …

We propose a novel approach for steganography using a reversible texture synthesis. A
texture synthesis process resamples a smaller texture image, which synthesizes a new …

We describe a system for capturing microscopic surface geometry. The system extends the
retrographic sensor [Johnson and Adelson 2009] to the microscopic domain, demonstrating …

Existing generative models for 3D shapes are typically trained on a large 3D dataset, often
of a specific object category. In this paper, we investigate the deep generative model that …

We tackle the problem of texture synthesis in the setting where many input images are given
and a large-scale output is required. We build on recent generative adversarial networks …

We present GenMM, a generative model that" mines" as many diverse motions as possible
from a single or few example sequences. In stark contrast to existing data-driven methods …

Image upsampling is a common yet challenging task, since it is severely underconstrained.
While considerable progress was made in preserving the sharpness of salient edges …