Non-Photorealistic Computer Graphics Library

Algorithms for Rendering in Artistic Styles

Author(s): Aaron Hertzmann.
PhD Thesis: New York University, May, 2001.
[BibTeX]

Abstract:
We describe new algorithms and tools for generating paintings, illustrations, and animation on a computer. These algorithms are designed to produce visually appealing and expressive images that look hand-painted or hand-drawn. In many contexts, painting and illustration have many advantages over photorealistic computer graphics, in aspects such as aesthetics, expression, and computational requirements. We explore three general strategies for non-photorealistic rendering: First, we describe explicit procedures for placing brush strokes. We begin with a painterly image processing algorithm inspired by painting with real physical media. This method produces images with a much greater subjective impression of looking hand-made than do earlier methods. By adjusting algorithm parameters, a variety of styles can be generated, such as styles inspired by the Impressionists and the Expressionists. This method is then extended to processing video, as demonstrated by painterly animations and an interactive installation. We then present a new style of line art illustration for smooth 3D surfaces. This style is designed to clearly convey surface shape, even for surfaces without predefined material properties or hatching directions. Next, we describe a new relaxation-based algorithm, in which we search for the painting that minimizes some energy function. In contrast to the first approach, we ideally only need to specify what we want, not how to directly compute it. The system allows as fine user control as desired: the user may interactively change the painting style, specify variations of style over an image, and/or add specific strokes to the painting. Finally, we describe a new framework for processing images by example, called “image analogies.” Given an example of a painting or drawing (e.g. scanned from a hand-painted source), we can process new images with some approximation to the style of the painting. In contrast to the first two approaches, this allows us to design styles without requiring an explicit technical definition of the style. The image analogies framework supports many other novel image processing operations.