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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 101–150 of 280 papers

TitleStatusHype
Two-Stream Convolutional Networks for Dynamic Texture SynthesisCode0
Hard Example Generation by Texture Synthesis for Cross-domain Shape Similarity LearningCode0
High resolution neural texture synthesis with long range constraintsCode0
A Generative Model for Texture Synthesis based on Optimal Transport between Feature DistributionsCode0
Enhancing Object Coherence in Layout-to-Image SynthesisCode0
On Using Backpropagation for Speech Texture Generation and Voice Conversion—0
Orthonormal Convolutions for the Rotation Based Iterative Gaussianization—0
Paint-it: Text-to-Texture Synthesis via Deep Convolutional Texture Map Optimization and Physically-Based Rendering—0
Parameter Sensitivity of Deep-Feature based Evaluation Metrics for Audio Textures—0
Pareidolia Face Reenactment—0
Patch-based Texture Synthesis for Image Inpainting—0
Paying U-Attention to Textures: Multi-Stage Hourglass Vision Transformer for Universal Texture Synthesis—0
Perception Driven Texture Generation—0
Periodicity Extraction using Superposition of Distance Matching Function and One-dimensional Haar Wavelet Transform—0
Perspective Texture Synthesis Based on Improved Energy Optimization—0
ID-Sculpt: ID-aware 3D Head Generation from Single In-the-wild Portrait Image—0
Projective Urban Texturing—0
Psychophysical vs. learnt texture representations in novelty detection—0
Reference-based OCT Angiogram Super-resolution with Learnable Texture Generation—0
Re-Identification Supervised Texture Generation—0
RoCoTex: A Robust Method for Consistent Texture Synthesis with Diffusion Models—0
RoomPainter: View-Integrated Diffusion for Consistent Indoor Scene Texturing—0
SeamlessGAN: Self-Supervised Synthesis of Tileable Texture Maps—0
Second-order Democratic Aggregation—0
Self-supervised Deformation Modeling for Facial Expression Editing—0
Semantic Image Translation for Repairing the Texture Defects of Building Models—0
Single Mesh Diffusion Models with Field Latents for Texture Generation—0
STS-GAN: Can We Synthesize Solid Texture with High Fidelity from Arbitrary 2D Exemplar?—0
Spatial Degradation-Aware and Temporal Consistent Diffusion Model for Compressed Video Super-Resolution—0
Step1X-3D: Towards High-Fidelity and Controllable Generation of Textured 3D Assets—0
Stochastic Geometry Models for Texture Synthesis of Machined Metallic Surfaces: Sandblasting and Milling—0
Structure Preserving Large Imagery Reconstruction—0
Strumming to the Beat: Audio-Conditioned Contrastive Video Textures—0
StyleTex: Style Image-Guided Texture Generation for 3D Models—0
“Style” Transfer for Musical Audio Using Multiple Time-Frequency Representations—0
Super-resolution Using Constrained Deep Texture Synthesis—0
Surface Geometry Processing: An Efficient Normal-based Detail Representation—0
Synthesis of Near-regular Natural Textures—0
TactStyle: Generating Tactile Textures with Generative AI for Digital Fabrication—0
TexDreamer: Towards Zero-Shot High-Fidelity 3D Human Texture Generation—0
TexFusion: Synthesizing 3D Textures with Text-Guided Image Diffusion Models—0
TexGarment: Consistent Garment UV Texture Generation via Efficient 3D Structure-Guided Diffusion Transformer—0
TexPro: Text-guided PBR Texturing with Procedural Material Modeling—0
TexRO: Generating Delicate Textures of 3D Models by Recursive Optimization—0
Text-Driven Diverse Facial Texture Generation via Progressive Latent-Space Refinement—0
Text-to-3D Generation with Bidirectional Diffusion using both 2D and 3D priors—0
Text-to-3D with Classifier Score Distillation—0
TextureDreamer: Image-guided Texture Synthesis through Geometry-aware Diffusion—0
Texture Generation Using Dual-Domain Feature Flow with Multi-View Hallucinations—0
Texture Image Synthesis Using Spatial GAN Based on Vision Transformers—0
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