Texture Synthesis

53 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

Most implemented papers

Texture Synthesis Using Convolutional Neural Networks

leongatys/DeepTextures NeurIPS 2015

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

Learning Texture Manifolds with the Periodic Spatial GAN

zalandoresearch/psgan ICML 2017

Second, we show that the image generation with PSGANs has properties of a texture manifold: we can smoothly interpolate between samples in the structured noise space and generate novel samples, which lie perceptually between the textures of the original dataset.

Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis

chuanli11/CNNMRF 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.

Stable and Controllable Neural Texture Synthesis and Style Transfer Using Histogram Losses

imransalam/style-transfer-tensorflow-2.0 31 Jan 2017

These losses can improve the quality of large features, improve the separation of content and style, and offer artistic controls such as paint by numbers.

Incorporating long-range consistency in CNN-based texture generation

guillaumebrg/texture_generation 3 Jun 2016

Gatys et al. (2015) showed that pair-wise products of features in a convolutional network are a very effective representation of image textures.

Texture Synthesis with Spatial Generative Adversarial Networks

zalandoresearch/spatial_gan 24 Nov 2016

Generative adversarial networks (GANs) are a recent approach to train generative models of data, which have been shown to work particularly well on image data.

EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis

msmsajjadi/EnhanceNet-Code ICCV 2017

Single image super-resolution is the task of inferring a high-resolution image from a single low-resolution input.

$μ$NCA: Texture Generation with Ultra-Compact Neural Cellular Automata

google-research/self-organising-systems 26 Nov 2021

We study the problem of example-based procedural texture synthesis using highly compact models.

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.

Texture Synthesis Through Convolutional Neural Networks and Spectrum Constraints

ngonthier/multiresolution_texture 4 May 2016

This paper presents a significant improvement for the synthesis of texture images using convolutional neural networks (CNNs), making use of constraints on the Fourier spectrum of the results.