Stroke Matching for Paint Dances

We have implemented a non-photorealistic rendering system which simulates the placement of paint/pencil/pastel strokes to produce representational artworks from digital images. The system is able to record an image of each paint stroke independent of the overall picture, in addition to some details about each stroke. Working with sets of paint strokes from paintings of different images, we investigate how to determine which stroke from one picture most closely resembles a given stroke from another picture. This enables the paint strokes from one picture to be used to paint a different painting. This further enables the animation of one picture morphing into another, as the paint strokes move and rotate into new positions and orientations. Using a K-means clustering approach, we can extract a set of representative strokes from a series of paintings/drawings, and animate the same set of strokes moving around a picture in order to represent different scenes at different times. We call such animations “paint dances”. We apply this technique to sets of portraits and we present the resulting paint dances in an artistic context as video art. We describe here the various methods we experimented with in order to determine an optimal stroke matching and extraction approach.