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

TL;DR

This paper explores combining classical stroke-based rendering techniques with neural network approaches to enhance artistic visualizations, aiming to leverage the strengths of both paradigms for more expressive non-photorealistic rendering.

Contribution

It introduces a framework that hybridizes classical and neural methods for stroke-based rendering, enabling new expressive possibilities and addressing limitations of each approach.

Findings

Classical NPR methods excel in artistic control and interpretability.

Neural methods offer higher quality and flexibility in rendering.

Hybrid approach combines strengths of both paradigms for improved results.

Abstract

Non-Photorealistic 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 (SBR), highlighting their strengths and limitations. We discuss trade offs in quality and artistic control between these paradigms, propose a framework where these approaches can be combined for new possibilities in expressive rendering.