page_title: Non-photorealistic rendering
section_title: 2D
hierarchical_section_title: Non-photorealistic rendering / 2D
caption_attribution_description: non-photorealistic rendering of File:Cradle_Mountain_Behind_Dove_Lake.jpg
context_page_description: Non-photorealistic rendering is an area of computer graphics that focuses on enabling a wide variety of expressive styles for digital art, in contrast to traditional computer graphics, which focuses on photorealism. NPR is inspired by other artistic modes such as painting, drawing, technical illustration, and animated cartoons. NPR has appeared in movies and video games in the form of cel-shaded animation as well as in scientific visualization, architectural illustration and experimental animation.
context_section_description: The input to a two dimensional NPR system is typically an image or video. The output is a typically an artistic rendering of that input imagery (for example in a watercolor, painterly or sketched style) although some 2D NPR serves non-artistic purposes e.g. data visualization.
  The artistic rendering of images and video (often referred to as Image Stylization) traditionally focused upon heuristic algorithms that seek to simulate the placement of brush strokes on a digital canvas.
  Arguably, the earliest example of 2D NPR is Paul Haeberli's 'Paint by Numbers' at SIGGRAPH 1990. This (and similar interactive techniques) provide the user with a canvas that they can "paint" on using the cursor — as the user paints, a stylized version of the image is revealed on the canvas. This is especially useful for people who want to simulate different sizes of brush strokes according to different areas of the image.
  Subsequently basic image processing operations using gradient operators or statistical moments were used to automate this process and minimize user interaction in the late nineties (although artistic control remains with the user via setting parameters of the algorithms). This automation enabled practical application of 2D NPR to video, for the first time in the living paintings of the movie What Dreams May Come (1998).
  More sophisticated image abstractions techniques were developed in the early 2000s harnessing Computer Vision operators e.g. image salience, or segmentation operators to drive stroke placement. Around this time, machine learning began to influence image stylization algorithms notably image analogy that could learn to mimic the style of an existing artwork.
  The advent of deep learning has re-kindled activity in image stylization, notably with Neural Style Transfer (NST) algorithms that can mimic a wide gamut of artistic styles from single visual examples. These algorithms underpin mobile apps capable of the same e.g. Prisma
  In addition to the above stylization methods, a related class of techniques in 2D NPR address the simulation of artistic media. These methods include simulating the diffusion of ink through different kinds of paper, and also of pigments through water for simulation of watercolor.
caption: A non-photorealistic rendering of an existing 2D (photographic) image
exif: {}