State of the ‘Art’: A Taxonomy of Artistic Stylization Techniques for Images and Video
This paper surveys the field of non-photorealistic rendering (NPR), focusing on techniques for transforming 2D input (images and video) into artistically stylized renderings. We first present a taxonomy of the 2D NPR algorithms developed over the past two decades, structured according to the design characteristics and behavior of each technique. We then describe a chronology of development from the semi-automatic paint systems of the early nineties, through to the automated painterly rendering systems of the late nineties driven by image gradient analysis. Two complementary trends in the NPR literature are then addressed, with reference to our taxonomy. First, the fusion of higher level computer vision and NPR, illustrating the trends toward scene analysis to drive artistic abstraction and diversity of style. Second, the evolution of local processing approaches toward edge-aware filtering for real-time stylization of images and video. The survey then concludes with a discussion of open challenges for 2D NPR identified in recent NPR symposia, including topics such as user and aesthetic evaluation.
This work was done in collaboration with the University of Surrey, UK, the Hasso-Plattner-Institut at the University of Potsdam, Germany, the Scientific Visualization and Computer Graphics Lab of the University of Groningen, the Netherlands, the AVIZ project group of INRIA, France, and the LIMSI lab at CNRS, France.