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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 226–250 of 280 papers

TitleStatusHype
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
Second-order Democratic Aggregation—0
Texture Mixing by Interpolating Deep Statistics via Gaussian Models—0
Non-Stationary Texture Synthesis by Adversarial ExpansionCode0
Learning the Synthesizability of Dynamic Texture Samples—0
Multiscale Sparse Microcanonical Models—0
Improved Style Transfer by Respecting Inter-layer Correlations—0
“Style” Transfer for Musical Audio Using Multiple Time-Frequency Representations—0
Texture Synthesis with Recurrent Variational Auto-EncoderCode0
On Using Backpropagation for Speech Texture Generation and Voice Conversion—0
Geometry Guided Adversarial Facial Expression Synthesis—0
Image Inpainting for High-Resolution Textures using CNN Texture Synthesis—0
GANosaic: Mosaic Creation with Generative Texture Manifolds—0
Fashioning with Networks: Neural Style Transfer to Design Clothes—0
A survey of exemplar-based texture synthesis—0
Synthesis of Near-regular Natural Textures—0
Two-Stream Convolutional Networks for Dynamic Texture SynthesisCode0
Graphcut Texture Synthesis for Single-Image Superresolution—0
TextureGAN: Controlling Deep Image Synthesis with Texture PatchesCode0
Towards Metamerism via Foveated Style TransferCode0
Learning Texture Manifolds with the Periodic Spatial GANCode0
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