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

Nonphotorealistic rendering (NPR) has long been used to create artistic visualizations that prioritize style over realism, enabling the depiction of a wide range of aesthetic effects, from hand-drawn sketches to painterly renderings. While classical NPR methods, such as edge detection, toon shading, and geometric abstraction, have been well established in both research and practice, with a particular focus on stroke-based rendering, the recent rise of deep learning represents a paradigm shift. We analyze the similarities and differences between classical and neural network-based NPR techniques, focusing on stroke-based rendering, highlighting their strengths and limitations. We discuss tradeoffs in quality and artistic control between these paradigms and propose a framework where these approaches can be combined for new possibilities in expressive rendering.