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Texture Synthesis

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

Papers

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Showing 251–280 of 280 papers

TitleStatusHype
A Procedural Texture Generation Framework Based on Semantic Descriptions—0
Perception Driven Texture Generation—0
Diversified Texture Synthesis with Feed-forward Networks—0
Stable and Controllable Neural Texture Synthesis and Style Transfer Using Histogram LossesCode0
Super-resolution Using Constrained Deep Texture Synthesis—0
Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis—0
EnhanceNet: Single Image Super-Resolution Through Automated Texture SynthesisCode0
On Random Weights for Texture Generation in One Layer Neural Networks—0
Awesome Typography: Statistics-Based Text Effects TransferCode0
Texture Synthesis with Spatial Generative Adversarial NetworksCode0
Bayesian Modeling of Motion Perception using Dynamical Stochastic Textures—0
Example-Based Image Synthesis via Randomized Patch-Matching—0
Style-Transfer via Texture-SynthesisCode0
Texture Synthesis Using Shallow Convolutional Networks with Random Filters—0
Patch-based Texture Synthesis for Image Inpainting—0
Texture Synthesis Through Convolutional Neural Networks and Spectrum ConstraintsCode0
Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial NetworksCode0
A note on the evaluation of generative modelsCode0
Texture Modelling with Nested High-order Markov-Gibbs Random Fields—0
Generative Image Modeling Using Spatial LSTMs—0
Texture Representations for Image and Video Synthesis—0
Texture Synthesis Using Convolutional Neural NetworksCode0
Automatic Objects Removal for Scene Completion—0
Facial Feature Point Detection: A Comprehensive Survey—0
Structure Preserving Large Imagery Reconstruction—0
Manifold Based Dynamic Texture Synthesis from Extremely Few Samples—0
Bayesian Active Appearance Models—0
The Synthesizability of Texture Examples—0
Periodicity Extraction using Superposition of Distance Matching Function and One-dimensional Haar Wavelet Transform—0
Nonparametric Bayesian Texture Learning and Synthesis—0
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