Texture Synthesis

40 papers with code • 0 benchmarks • 2 datasets

The fundamental goal of example-based Texture Synthesis is to generate a texture, usually larger than the input, that faithfully captures all the visual characteristics of the exemplar, yet is neither identical to it, nor exhibits obvious unnatural looking artifacts.

Source: Non-Stationary Texture Synthesis by Adversarial Expansion

Greatest papers with code

Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis

awentzonline/image-analogies CVPR 2016

This paper studies a combination of generative Markov random field (MRF) models and discriminatively trained deep convolutional neural networks (dCNNs) for synthesizing 2D images.

Image Generation Texture Synthesis

Texture Memory-Augmented Deep Patch-Based Image Inpainting

open-mmlab/mmediting 28 Sep 2020

By bringing together the best of both paradigms, we propose a new deep inpainting framework where texture generation is guided by a texture memory of patch samples extracted from unmasked regions.

Image Inpainting Texture Synthesis

Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis

DmitryUlyanov/texture_nets CVPR 2017

The recent work of Gatys et al., who characterized the style of an image by the statistics of convolutional neural network filters, ignited a renewed interest in the texture generation and image stylization problems.

Image Generation Image Stylization +1

Texture Synthesis Using Convolutional Neural Networks

DmitryUlyanov/texture_nets NeurIPS 2015

Here we introduce a new model of natural textures based on the feature spaces of convolutional neural networks optimised for object recognition.

Object Recognition Texture Synthesis

Learning Texture Transformer Network for Image Super-Resolution

researchmm/TTSR CVPR 2020

In this paper, we propose a novel Texture Transformer Network for Image Super-Resolution (TTSR), in which the LR and Ref images are formulated as queries and keys in a transformer, respectively.

Image Generation Image Super-Resolution +1

Non-Stationary Texture Synthesis by Adversarial Expansion

jessemelpolio/non-stationary_texture_syn 11 May 2018

We demonstrate that this conceptually simple approach is highly effective for capturing large-scale structures, as well as other non-stationary attributes of the input exemplar.

Texture Synthesis

Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks

chuanli11/MGANs 15 Apr 2016

This paper proposes Markovian Generative Adversarial Networks (MGANs), a method for training generative neural networks for efficient texture synthesis.

Style Transfer Texture Synthesis

StructureFlow: Image Inpainting via Structure-aware Appearance Flow

RenYurui/StructureFlow ICCV 2019

Image inpainting techniques have shown significant improvements by using deep neural networks recently.

Image Inpainting Texture Synthesis

Two-Stream Convolutional Networks for Dynamic Texture Synthesis

ryersonvisionlab/two-stream-dyntex-synth CVPR 2018

Given an input dynamic texture, statistics of filter responses from the object recognition ConvNet encapsulate the per-frame appearance of the input texture, while statistics of filter responses from the optical flow ConvNet model its dynamics.

Object Recognition Optical Flow Estimation +2

TileGAN: Synthesis of Large-Scale Non-Homogeneous Textures

afruehstueck/tileGAN 29 Apr 2019

We tackle the problem of texture synthesis in the setting where many input images are given and a large-scale output is required.

Image Generation Image Stylization +1