SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 201–250 of 280 papers

TitleStatusHype
Modeling Caricature Expressions by 3D Blendshape and Dynamic Texture—0
High resolution neural texture synthesis with long range constraintsCode0
Interpreting Spatially Infinite Generative Models—0
Transposer: Universal Texture Synthesis Using Feature Maps as Transposed Convolution Filter—0
GramGAN: Deep 3D Texture Synthesis From 2D Exemplars—0
Perspective Texture Synthesis Based on Improved Energy Optimization—0
A Generative Model for Texture Synthesis based on Optimal Transport between Feature DistributionsCode0
Texture Interpolation for Probing Visual PerceptionCode0
Fast Texture Synthesis via Pseudo OptimizerCode0
Co-occurrence Based Texture SynthesisCode0
Guidance and Evaluation: Semantic-Aware Image Inpainting for Mixed Scenes—0
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
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
Show:102550
← PrevPage 5 of 6Next →

No leaderboard results yet.