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20182024
most citedOn Demand Solid Texture Synthesis Using Deep 3D Networks

25 citations · 29 across the 8 of their papers we have counts for

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6 papers · 1 filter

cs.CV2024

SGSST: Scaling Gaussian Splatting StyleTransfer

Bruno Galerne, Jianling Wang, Lara Raad +1

Applying style transfer to a full 3D environment is a challenging task that has seen many developments since the advent of neural rendering. 3D Gaussian splatting (3DGS) has recent…

cs.CV2024

Adapting MIMO video restoration networks to low latency constraints

Valéry Dewil, Zhe Zheng, Arnaud Barral +7

MIMO (multiple input, multiple output) approaches are a recent trend in neural network architectures for video restoration problems, where each network evaluation produces multiple…

cs.CV2023

On The Role of Alias and Band-Shift for Sentinel-2 Super-Resolution

Ngoc Long Nguyen, Jérémy Anger, Lara Raad +2

In this work, we study the problem of single-image super-resolution (SISR) of Sentinel-2 imagery. We show that thanks to its unique sensor specification, namely the inter-band shif…

cs.CV2022★ 2 cited

Scaling Painting Style Transfer

Bruno Galerne, Lara Raad, José Lezama +1

Neural style transfer (NST) is a deep learning technique that produces an unprecedentedly rich style transfer from a style image to a content image. It is particularly impressive w…

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.CV2019★ 1 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…