Semantically meaningful vectorization of line art in drawn animation

Abstract

Animation consists of sequentially showing multiple single frames with small mutual differences in order to achieve the visual effect of a moving scene. In limited animation, these frames are drawn as semantically meaningful vector images which could be referred to as clean animation frames. There are limited animation workflows in which these clean animation frames are only available in raster format, requiring laborious manual vectorization.This work explores the extent to which line-art image vectorization methods can be used to automatize this process. For this purpose, a line-art image vectorization method is designed by taking into account the structural information about clean animation frames. Together with existing state-of-the-art line-art image vectorization methods, this method is evaluated on a dataset consisting of clean animation frames. The reproducible evaluation shows that the performance of the developed method is remarkably stable across different input image resolution sizes and binarized or non-binarized versions of input images, even outperforming state-of-the-art methods at input images of the default clean animation frame resolution. Furthermore, it is up to 4.5 times faster than the second-fastest deep learning-based method. However, ultimately the evaluation shows that neither the developed method nor existing state-of-the-art methods can produce vector images that achieve both visual similarity and sufficiently semantically correct vector structures.