Interactive cartoon reusing by transfer learning
Signal Processing
Transductive cartoon retrieval by multiple hypergraph learning
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
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To reduce tedious works 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 Transductive Graph based Cartoon Synthesis (TGCS) approach proposed in this paper aims at synthesizing 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 rotation, and scaling invariant. According to the similarity, the label propagation based graph transduction method is adopted to generate cartoon clips, which is smoother than the clips generated by the shortest path method used in previous cartoon reusing approaches. Besides, the synthesized cartoon clips can be applied in accurate correspondence building, based on which the inbetweening method can be used to refine the results. Experimental results on our cartoon dataset suggest the effectiveness of the proposed approach for cartoon synthesis. Additional experiments on correspondence show our approach's performance on accurate correspondence building. Copyright © 2010 John Wiley & Sons, Ltd. Though the inbetweening is quite important for cartoon synthesis, it is not discussed in this paper. In future, we plan to develop an efficient inbetweening method based on the constructed correspondence shown in Fig. 9.