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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 151–160 of 280 papers

TitleStatusHype
TUVF: Learning Generalizable Texture UV Radiance Fields—0
Generating Texture for 3D Human Avatar from a Single Image using Sampling and Refinement Networks—0
Semantic Image Translation for Repairing the Texture Defects of Building Models—0
A geometrically aware auto-encoder for multi-texture synthesisCode0
DyNCA: Real-time Dynamic Texture Synthesis Using Neural Cellular Automata—0
Long Range Constraints for Neural Texture Synthesis Using Sliced Wasserstein LossCode0
A Structure-Guided Diffusion Model for Large-Hole Image CompletionCode0
Parameter Sensitivity of Deep-Feature based Evaluation Metrics for Audio Textures—0
Initialization and Alignment for Adversarial Texture Optimization—0
Texture Generation Using A Graph Generative Adversarial Network And Differentiable RenderingCode0
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