Texture Synthesis
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Greatest papers with code
Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis
CVPR 2016
• awentzonline/image-analogies
• 
This paper studies a combination of generative Markov random field (MRF) models and discriminatively trained deep convolutional neural networks (dCNNs) for synthesizing 2D images.
Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis
CVPR 2017
• DmitryUlyanov/texture_nets
• 
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.
Texture Synthesis Using Convolutional Neural Networks
NeurIPS 2015
• DmitryUlyanov/texture_nets
•
Here we introduce a new model of natural textures based on the feature spaces of convolutional neural networks optimised for object recognition.
Non-Stationary Texture Synthesis by Adversarial Expansion
• jessemelpolio/non-stationary_texture_syn
• 
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.
Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks
• chuanli11/MGANs
•
This paper proposes Markovian Generative Adversarial Networks (MGANs), a method for training generative neural networks for efficient texture synthesis.
Two-Stream Convolutional Networks for Dynamic Texture Synthesis
CVPR 2018
• ryersonvisionlab/two-stream-dyntex-synth
•
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 STYLE TRANSFER TEXTURE SYNTHESIS
EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis
ICCV 2017
• msmsajjadi/EnhanceNet-Code
•
Single image super-resolution is the task of inferring a high-resolution image from a single low-resolution input.
#13 best model for
Image Super-Resolution
on BSD100 - 4x upscaling
TileGAN: Synthesis of Large-Scale Non-Homogeneous Textures
We tackle the problem of texture synthesis in the setting where many input images are given and a large-scale output is required.
Learning Texture Manifolds with the Periodic Spatial GAN
ICML 2017
• zalandoresearch/psgan
•
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.
Texture Synthesis with Spatial Generative Adversarial Networks
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.