Texture Synthesis
Generating new, larger versions of a texture by learning its statistical patterns and extrapolating.
Think of it like
A piece of bark: from a small sample, you infer the full tree and generate more bark that looks consistent.
Example
Input: a 64x64 fabric swatch. Output: a seamless 1024x1024 image of the same fabric. Or grass, waves, marble patterns.
How it actually works
Statistical methods (e.g., Markov random fields, graph cuts) match local neighborhoods. Neural approaches learn texture statistics via CNNs and feed-forward or diffusion models. Conditional generation via example patches. Quality degrades if the texture has large structures or orientations. Generative models (GANs, diffusion) now dominate. Increasingly replaced by diffusion-based in-painting.
For product teams
Graphics and game development, but replaced by diffusion models for most use cases.
For engineers
Classic: non-parametric sampling. Modern: conditional diffusion or feed-forward network trained on texture pairs.
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