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Updated: Aug 30, 2025

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Arbitrary-Scale Texture Generation From Coarse-Grained Control.

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    This study introduces a novel deep learning method for texture synthesis using high-level guidance instead of specific exemplars. The approach enables real-time generation of diverse, controllable textures through a Bayesian hierarchical model.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Current deep-network texture synthesis methods rely on exemplar-based generation, requiring numerous networks for diverse textures.
    • This limits flexibility and scalability in texture modeling.

    Purpose of the Study:

    • To develop a unified framework for texture generation directly from high-level guidance.
    • To enable real-time synthesis of diverse textures with arbitrary scales and user-defined control.

    Main Methods:

    • A three-level Bayesian hierarchical model is proposed to parse texture generation.
    • A coarse-grained signal determines a distribution over Markov random fields (MRFs).
    • A fully convolutional network is utilized at the base of the hierarchy for texture generation.

    Main Results:

    • The method successfully generates plausible textures faithful to high-level controls like categories and semantic descriptions.
    • Demonstrates impressive texture metamorphosis via interpolation within the learned manifold.
    • Achieves real-time texture generation by integrating creation and synthesis.

    Conclusions:

    • The proposed Bayesian hierarchical model offers a flexible and efficient approach to texture synthesis.
    • Enables direct generation of textures from semantic information, overcoming limitations of exemplar-based methods.
    • Facilitates diverse texture creation and manipulation for various applications.