Semi-automatic cartoon generation by motion planning

To reduce tedious work in cartoon animation, some computer-assisted systems including automatic Inbetweening and cartoon reusing systems have been proposed. In existing automatic Inbetweening systems, accurate correspondence construction, which is a prerequisite for Inbetweening, cannot be achieved. For cartoon reusing systems, the lack of efficient similarity estimation method and reusing mechanism makes it impractical for the users. The semi-supervised graph-based cartoon reusing approach proposed in this paper aims at generating smooth cartoons from the existing data. In this approach, the similarity between cartoon frames can be accurately evaluated by calculating the distance based on local shape context, which is expected to be rotation and scaling invariant. By the semi-supervised algorithm, given an initial frame, the most similar cartoon frames in the cartoon library are selected as candidates of the next frame. The smooth cartoons can be generated by carrying out the algorithm repeatedly to select new cartoon frames after the cartoonists specifying the motion path in a background image. Experimental results of the candidate frame selection in our cartoon dataset suggest the effectiveness of the proposed local shape context for similarity evaluation. The other experiments show the excellent performance on cartoon generation of our approach.