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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
Video-driven Neural Physically-based Facial Asset for Production—0
Volumetric Temporal Texture Synthesis for Smoke Stylization using Neural Cellular Automata—0
Wallpaper Texture Generation and Style Transfer Based on Multi-label Semantics—0
Wasserstein Distortion: Unifying Fidelity and Realism—0
Weakly-Supervised Photo-realistic Texture Generation for 3D Face Reconstruction—0
WordRobe: Text-Guided Generation of Textured 3D Garments—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
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
Non-Stationary Texture Synthesis by Adversarial ExpansionCode0
Deep Audio PriorCode0
μNCA: Texture Generation with Ultra-Compact Neural Cellular AutomataCode0
Style-Transfer via Texture-SynthesisCode0
Two-Stream Convolutional Networks for Dynamic Texture SynthesisCode0
Co-occurrence Based Texture SynthesisCode0
Conditional Generative ConvNets for Exemplar-based Texture SynthesisCode0
Hard Example Generation by Texture Synthesis for Cross-domain Shape Similarity LearningCode0
A Structure-Guided Diffusion Model for Large-Hole Image CompletionCode0
Long Range Constraints for Neural Texture Synthesis Using Sliced Wasserstein LossCode0
Local Padding in Patch-Based GANs for Seamless Infinite-Sized Texture SynthesisCode0
Generalized Rectifier Wavelet Covariance Models For Texture SynthesisCode0
Texture Synthesis Through Convolutional Neural Networks and Spectrum ConstraintsCode0
Texture Synthesis Using Convolutional Neural NetworksCode0
Towards Metamerism via Foveated Style TransferCode0
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