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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 51–75 of 280 papers

TitleStatusHype
ClipFace: Text-guided Editing of Textured 3D Morphable ModelsCode1
MeshSegmenter: Zero-Shot Mesh Semantic Segmentation via Texture SynthesisCode1
Texture Generation with Neural Cellular AutomataCode1
DeepDC: Deep Distance Correlation as a Perceptual Image Quality EvaluatorCode1
Text2Tex: Text-driven Texture Synthesis via Diffusion ModelsCode1
Text-Guided 3D Face Synthesis -- From Generation to EditingCode1
FeatureFlow: Robust Video Interpolation via Structure-to-Texture GenerationCode1
Generating Diverse Structure for Image Inpainting With Hierarchical VQ-VAECode1
3D-FRONT: 3D Furnished Rooms with layOuts and semaNTicsCode1
HiPrompt: Tuning-free Higher-Resolution Generation with Hierarchical MLLM PromptsCode1
Text-Guided 3D Face Synthesis - From Generation to EditingCode1
Kernelized Similarity Learning and Embedding for Dynamic Texture SynthesisCode0
Conditional Generative ConvNets for Exemplar-based Texture SynthesisCode0
A Structure-Guided Diffusion Model for Large-Hole Image CompletionCode0
Stable and Controllable Neural Texture Synthesis and Style Transfer Using Histogram LossesCode0
Semantics-Aligned Representation Learning for Person Re-identificationCode0
Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial NetworksCode0
StructureFlow: Image Inpainting via Structure-aware Appearance FlowCode0
A note on the evaluation of generative modelsCode0
Non-Stationary Texture Synthesis by Adversarial ExpansionCode0
μNCA: Texture Generation with Ultra-Compact Neural Cellular AutomataCode0
LeFusion: Controllable Pathology Synthesis via Lesion-Focused Diffusion ModelsCode0
Long Range Constraints for Neural Texture Synthesis Using Sliced Wasserstein LossCode0
Does resistance to style-transfer equal Global Shape Bias? Measuring network sensitivity to global shape configurationCode0
Style-Transfer via Texture-SynthesisCode0
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