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  3. Hybridizing Expressive Rendering: Stroke-based Rendering With Classic And Neural Methods.
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  3. Hybridizing Expressive Rendering: Stroke-based Rendering With Classic And Neural Methods.

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Hybridizing Expressive Rendering: Stroke-Based Rendering With Classic and Neural Methods.

Kapil Dev, Rahul C Basole, Francesco Ferrise

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    Summary
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    Classical and deep learning methods for nonphotorealistic rendering (NPR) offer distinct advantages. This study compares these approaches, particularly for stroke-based rendering, and proposes a framework for combining them for enhanced artistic visualizations.

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    Area of Science:

    • Computer Graphics
    • Artificial Intelligence

    Background:

    • Nonphotorealistic rendering (NPR) traditionally uses techniques like edge detection and toon shading for artistic effects.
    • Stroke-based rendering is a key area within classical NPR, focusing on simulating artistic strokes.
    • Deep learning has emerged as a significant advancement, offering new paradigms for NPR.

    Purpose of the Study:

    • To analyze and compare classical and neural network-based NPR techniques.
    • To highlight the strengths and limitations of both approaches, especially in stroke-based rendering.
    • To propose a framework for integrating classical and deep learning methods for novel expressive rendering.

    Main Methods:

    • Comparative analysis of classical NPR methods (edge detection, toon shading, geometric abstraction) and deep learning-based NPR.
  • Focus on stroke-based rendering techniques within both paradigms.
  • Exploration of quality and artistic control trade-offs.

    • Main Results:

    • Classical NPR excels in established artistic styles and control.
    • Deep learning-based NPR offers novel aesthetic possibilities but may require more data and computational resources.
    • Both approaches have unique strengths and limitations in achieving expressive rendering.

    Conclusions:

    • A hybrid framework combining classical and deep learning NPR can unlock new creative potentials.
    • Integrating these methods allows for a balance of artistic control and novel stylistic generation.
    • Future research can explore synergistic combinations for advanced NPR applications.