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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 201–210 of 280 papers

TitleStatusHype
DragTex: Generative Point-Based Texture Editing on 3D Mesh—0
DreamPolish: Domain Score Distillation With Progressive Geometry Generation—0
DreamSpace: Dreaming Your Room Space with Text-Driven Panoramic Texture Propagation—0
Dress-1-to-3: Single Image to Simulation-Ready 3D Outfit with Diffusion Prior and Differentiable Physics—0
DressCode: Autoregressively Sewing and Generating Garments from Text Guidance—0
DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network—0
Dual Pipeline Style Transfer with Input Distribution Differentiation—0
Dynamic Neural Style Transfer for Artistic Image Generation using VGG19—0
Dynamic Texture Synthesis by Incorporating Long-range Spatial and Temporal Correlations—0
DyNCA: Real-time Dynamic Texture Synthesis Using Neural Cellular Automata—0
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