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
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.
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
StructureFlow: Image Inpainting via Structure-aware Appearance Flow
ICCV 2019
• RenYurui/StructureFlow
•
Image inpainting techniques have shown significant improvements by using deep neural networks recently.
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.