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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 226–250 of 280 papers

TitleStatusHype
FruitNinja: 3D Object Interior Texture Generation with Gaussian Splatting—0
GANosaic: Mosaic Creation with Generative Texture Manifolds—0
Generating Texture for 3D Human Avatar from a Single Image using Sampling and Refinement Networks—0
Generative AI meets 3D: A Survey on Text-to-3D in AIGC Era—0
Generative Image Modeling Using Spatial LSTMs—0
Generator Pyramid for High-Resolution Image Inpainting—0
GenesisTex2: Stable, Consistent and High-Quality Text-to-Texture Generation—0
Geometry-Aware Texture Generation for 3D Head Modeling with Artist-driven Control—0
Geometry Guided Adversarial Facial Expression Synthesis—0
GramGAN: Deep 3D Texture Synthesis From 2D Exemplars—0
Graphcut Texture Synthesis for Single-Image Superresolution—0
Guidance and Evaluation: Semantic-Aware Image Inpainting for Mixed Scenes—0
HumanNorm: Learning Normal Diffusion Model for High-quality and Realistic 3D Human Generation—0
ID-to-3D: Expressive ID-guided 3D Heads via Score Distillation Sampling—0
Image Inpainting for High-Resolution Textures using CNN Texture Synthesis—0
Improved Style Transfer by Respecting Inter-layer Correlations—0
Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis—0
Infinite Texture: Text-guided High Resolution Diffusion Texture Synthesis—0
Initialization and Alignment for Adversarial Texture Optimization—0
InsTex: Indoor Scenes Stylized Texture Synthesis—0
Integrating GAN and Texture Synthesis for Enhanced Road Damage Detection—0
Interpreting Spatially Infinite Generative Models—0
InTeX: Interactive Text-to-texture Synthesis via Unified Depth-aware Inpainting—0
Learning an Action-Conditional Model for Haptic Texture Generation—0
Learning in a Single Domain for Non-Stationary Multi-Texture Synthesis—0
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