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Texture Synthesis

15 papers with code · Computer Vision

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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.

IMAGE GENERATION TEXTURE SYNTHESIS

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.

IMAGE GENERATION IMAGE STYLIZATION TEXTURE SYNTHESIS

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.

OBJECT RECOGNITION TEXTURE SYNTHESIS

Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks

15 Apr 2016chuanli11/MGANs

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

STYLE TRANSFER 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

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.

IMAGE GENERATION TEXTURE SYNTHESIS

Texture Synthesis with Spatial Generative Adversarial Networks

24 Nov 2016ubergmann/spatial_gan

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.

TEXTURE SYNTHESIS

TileGAN: Synthesis of Large-Scale Non-Homogeneous Textures

29 Apr 2019afruehstueck/tileGAN

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 TEXTURE SYNTHESIS