computer-vision-1
Texture Synthesis
3260 papers • 126 benchmarks • 313 datasets
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
(Image credit: Papersgraph)
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Most implemented papers
Texture Synthesis Using Convolutional Neural Networks
M. Bethge, Alexander S. Ecker, Leon A. Gatys•Tue May 26 2015
A new model of natural textures based on the feature spaces of convolutional neural networks optimised for object recognition is introduced, showing that across layers the texture representations increasingly capture the statistical properties of natural images while making object information more and more explicit.
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