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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 201–250 of 280 papers

TitleStatusHype
Learning Texture Transformer Network for Image Super-ResolutionCode1
Texture Interpolation for Probing Visual PerceptionCode0
Fast Texture Synthesis via Pseudo OptimizerCode0
FeatureFlow: Robust Video Interpolation via Structure-to-Texture GenerationCode1
Toward a Universal Model for Shape From TextureCode1
Co-occurrence Based Texture SynthesisCode0
Adversarial Texture Optimization from RGB-D ScansCode1
Guidance and Evaluation: Semantic-Aware Image Inpainting for Mixed Scenes—0
On Demand Solid Texture Synthesis Using Deep 3D NetworksCode1
Deep Audio PriorCode0
One-Stage Inpainting with Bilateral Attention and Pyramid Filling Block—0
Conditional Generative ConvNets for Exemplar-based Texture SynthesisCode0
Maximum entropy methods for texture synthesis: theory and practice—0
Kernelized Similarity Learning and Embedding for Dynamic Texture SynthesisCode0
Self-supervised Deformation Modeling for Facial Expression Editing—0
Learning an Action-Conditional Model for Haptic Texture Generation—0
Three-D Safari: Learning to Estimate Zebra Pose, Shape, and Texture from Images "In the Wild"Code0
StructureFlow: Image Inpainting via Structure-aware Appearance FlowCode0
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
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
Graphcut Texture Synthesis for Single-Image Superresolution—0
Two-Stream Convolutional Networks for Dynamic Texture SynthesisCode0
TextureGAN: Controlling Deep Image Synthesis with Texture PatchesCode0
Towards Metamerism via Foveated Style TransferCode0
Learning Texture Manifolds with the Periodic Spatial GANCode0
A Procedural Texture Generation Framework Based on Semantic Descriptions—0
Perception Driven Texture Generation—0
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