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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

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Datasets

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.

IMAGE GENERATION TEXTURE SYNTHESIS

Texture Memory-Augmented Deep Patch-Based Image Inpainting

28 Sep 2020open-mmlab/mmediting

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

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

Learning Texture Transformer Network for Image Super-Resolution

CVPR 2020 researchmm/TTSR

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 TEXTURE SYNTHESIS

Non-Stationary Texture Synthesis by Adversarial Expansion

11 May 2018jessemelpolio/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.

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

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

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.

IMAGE INPAINTING TEXTURE SYNTHESIS