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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 251–280 of 280 papers

TitleStatusHype
Learning the Synthesizability of Dynamic Texture Samples—0
Level generation and style enhancement -- deep learning for game development overview—0
Macrocanonical Models for Texture Synthesis—0
Make-A-Texture: Fast Shape-Aware Texture Generation in 3 Seconds—0
Make-It-Vivid: Dressing Your Animatable Biped Cartoon Characters from Text—0
Manifold Based Dynamic Texture Synthesis from Extremely Few Samples—0
Marginal Contrastive Correspondence for Guided Image Generation—0
MaterialMVP: Illumination-Invariant Material Generation via Multi-view PBR Diffusion—0
Maximum entropy methods for texture synthesis: theory and practice—0
MD-ProjTex: Texturing 3D Shapes with Multi-Diffusion Projection—0
Mesh Neural Cellular Automata—0
Meta 3D Gen—0
Meta 3D TextureGen: Fast and Consistent Texture Generation for 3D Objects—0
Minecraft-ify: Minecraft Style Image Generation with Text-guided Image Editing for In-Game Application—0
Modeling Caricature Expressions by 3D Blendshape and Dynamic Texture—0
MoE-DiffIR: Task-customized Diffusion Priors for Universal Compressed Image Restoration—0
MTCRNN: A multi-scale RNN for directed audio texture synthesis—0
Multiscale Sparse Microcanonical Models—0
Multispectral Texture Synthesis using RGB Convolutional Neural Networks—0
Multi-Texture Synthesis through Signal Responsive Neural Cellular Automata—0
MVPainter: Accurate and Detailed 3D Texture Generation via Multi-View Diffusion with Geometric Control—0
NeRF-Texture: Synthesizing Neural Radiance Field Textures—0
Neural FFTs for Universal Texture Image Synthesis—0
NITES: A Non-Parametric Interpretable Texture Synthesis Method—0
NoiseNCA: Noisy Seed Improves Spatio-Temporal Continuity of Neural Cellular Automata—0
Nonparametric Bayesian Texture Learning and Synthesis—0
Occlusion-Aware Video Object Inpainting—0
One-Stage Inpainting with Bilateral Attention and Pyramid Filling Block—0
On Random Weights for Texture Generation in One Layer Neural Networks—0
On Synthetic Texture Datasets: Challenges, Creation, and Curation—0
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