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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 221–230 of 280 papers

TitleStatusHype
DynTypo: Example-Based Dynamic Text Effects Transfer—0
Semantics-Aligned Representation Learning for Person Re-identificationCode0
Psychophysical vs. learnt texture representations in novelty detection—0
TileGAN: Synthesis of Large-Scale Non-Homogeneous TexturesCode0
Macrocanonical Models for Texture Synthesis—0
User-Controllable Multi-Texture Synthesis with Generative Adversarial Networks—0
Re-Identification Supervised Texture Generation—0
Texture Synthesis Guided Deep Hashing for Texture Image Retrieval—0
FrankenGAN: Guided Detail Synthesis for Building Mass-Models Using Style-Synchronized GANsCode0
Context-Aware Text-Based Binary Image Stylization and Synthesis—0
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