The real world exhibits an abundance of non-stationary textures. Examples include textures
with large-scale structures, as well as spatially variant and inhomogeneous textures. While …

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 introduce a novel approach to example-based stylization of portrait videos that
preserves both the subject's identity and the visual richness of the input style exemplar …

In this paper we present Diffusion Image Analogies—an example-based image editing
approach that builds upon the concept of image analogies originally introduced by …

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 …

Markov random fields (MRFs) are the cornerstone of classical approaches to example-
based texture synthesis. Yet, it is not fully valued in the deep learning era. This paper aims to …

This paper addresses the challenge of example-based non-stationary texture synthesis. We
introduce a novel two-step approach wherein users first modify a reference texture using …

We present StyleBlit—an efficient example‐based style transfer algorithm that can deliver
high‐quality stylized renderings in real‐time on a single‐core CPU. Our technique is …

Artificial terrain synthesis is challenging because natural terrain contains many types of
features generated by a diverse range of natural processes over vastly different time-scales …

We propose MatUp, an upsampling filter for material super‐resolution. Our method takes as
input a low‐resolution SVBRDF and upscales its maps so that their rendering under various …