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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 126–150 of 280 papers

TitleStatusHype
3DGen: Triplane Latent Diffusion for Textured Mesh GenerationCode2
A geometrically aware auto-encoder for multi-texture synthesisCode0
Neural Texture Synthesis With Guided CorrespondenceCode1
ClipFace: Text-guided Editing of Textured 3D Morphable ModelsCode1
Long Range Constraints for Neural Texture Synthesis Using Sliced Wasserstein LossCode0
DyNCA: Real-time Dynamic Texture Synthesis Using Neural Cellular Automata—0
A Structure-Guided Diffusion Model for Large-Hole Image CompletionCode0
DeepDC: Deep Distance Correlation as a Perceptual Image Quality EvaluatorCode1
Parameter Sensitivity of Deep-Feature based Evaluation Metrics for Audio Textures—0
Keys to Better Image Inpainting: Structure and Texture Go Hand in HandCode1
Initialization and Alignment for Adversarial Texture Optimization—0
Texture Generation Using A Graph Generative Adversarial Network And Differentiable RenderingCode0
Orthonormal Convolutions for the Rotation Based Iterative Gaussianization—0
Pretraining is All You Need for Image-to-Image TranslationCode2
AvatarCLIP: Zero-Shot Text-Driven Generation and Animation of 3D AvatarsCode3
Emotion-Controllable Generalized Talking Face Generation—0
AUV-Net: Learning Aligned UV Maps for Texture Transfer and Synthesis—0
Exemplar-based Pattern Synthesis with Implicit Periodic Field Network—0
3D microstructural generation from 2D images of cement paste using generative adversarial networksCode0
Marginal Contrastive Correspondence for Guided Image Generation—0
Generalized Rectifier Wavelet Covariance Models For Texture SynthesisCode0
Texture Generation Using Dual-Domain Feature Flow with Multi-View Hallucinations—0
Towards Universal Texture Synthesis by Combining Texton Broadcasting with Noise Injection in StyleGAN-2Code1
Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution PriorsCode2
Paying U-Attention to Textures: Multi-Stage Hourglass Vision Transformer for Universal Texture Synthesis—0
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