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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 191–200 of 280 papers

TitleStatusHype
Consistent Zero-shot 3D Texture Synthesis Using Geometry-aware Diffusion and Temporal Video Models—0
Context-Aware Text-Based Binary Image Stylization and Synthesis—0
CTGAN: Semantic-guided Conditional Texture Generator for 3D Shapes—0
Deep Tiling: Texture Tile Synthesis Using a Deep Learning Approach—0
Dessie: Disentanglement for Articulated 3D Horse Shape and Pose Estimation from Images—0
Differentiable Programming of Reaction-Diffusion Patterns—0
Diff-Lung: Diffusion-Based Texture Synthesis for Enhanced Pathological Tissue Segmentation in Lung CT Scans—0
Diffusion-based Holistic Texture Rectification and Synthesis—0
Disentangled Pose and Appearance Guidance for Multi-Pose Generation—0
Diversified Texture Synthesis with Feed-forward Networks—0
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