activity
20182020
most citedOn Demand Solid Texture Synthesis Using Deep 3D Networks

25 citations · 26 across the 3 of their papers we have counts for

collaborators

5 papers

cs.GR202025 cited

On Demand Solid Texture Synthesis Using Deep 3D Networks

Jorge Gutierrez, Julien Rabin, Bruno Galerne +1

This paper describes a novel approach for on demand volumetric texture synthesis based on a deep learning framework that allows for the generation of high quality 3D data at intera…

math.ST2019

Maximum entropy methods for texture synthesis: theory and practice

Valentin De Bortoli, Agnes Desolneux, Alain Durmus +2

Recent years have seen the rise of convolutional neural network techniques in exemplar-based image synthesis. These methods often rely on the minimization of some variational formu…

cs.CV2019

Patch redundancy in images: a statistical testing framework and some applications

De Bortoli Valentin, Desolneux Agnès, Galerne Bruno +1

In this work we introduce a statistical framework in order to analyze the spatial redundancy in natural images. This notion of spatial redundancy must be defined locally and thus w…

cs.CV20191 cited

Macrocanonical Models for Texture Synthesis

De Bortoli Valentin, Desolneux Agnès, Galerne Bruno +1

In this article we consider macrocanonical models for texture synthesis. In these models samples are generated given an input texture image and a set of features which should be ma…

stat.ML2018

Exact Sampling of Determinantal Point Processes without Eigendecomposition

Claire Launay, Bruno Galerne, Agnès Desolneux

Determinantal point processes (DPPs) enable the modeling of repulsion: they provide diverse sets of points. The repulsion is encoded in a kernel that can be seen as a matrix st…