DBSC‐based animation enhanced with feature and motion

Chen, Quan; Tian, Feng; Seah, Hock Soon; Wu, Zhang; Qiu, Jie; Melikhov, Konstantin

doi:10.1002/cav.122

Cited by 22 publications

(12 citation statements)

References 13 publications

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“…But the mismatches can be solved by using the transductive methods shown in Figure 9 (b). More correspondence results are presented in the pictures (4), (5), and (6) of Figure 9 (b). In Figure 9 (c), the correspondence is built for Jerry.…”

Section: Experiments On Correspondence Buildingmentioning

confidence: 95%

Transductive graph based cartoon synthesis

Liu²,

Seah³

2010

Computer Animation & Virtual

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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.

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Section: Experiments On Correspondence Buildingmentioning

confidence: 95%

Transductive graph based cartoon synthesis

Liu²,

Seah³

2010

Computer Animation & Virtual

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“…This approach is designed for relative simple animation styles, such as cut-out animation. Similar approach proposed in [25] also applies heuristic matching method on feature points detected by geometric analysis. The optimization of matching is not global.…”

Section: Correspondence-based Methodsmentioning

confidence: 99%

Semi-automatic cartoon generation by motion planning

Tao

Wang

et al. 2010

Multimedia Systems

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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.

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“…By interpolating both positions and thickness (obtained from pressures the user puts on the stylus) of two or more corresponding DBSCs, inbetweens can be generated to form a smooth animation sequence. Our works in inbetweening were reported in [6], [7].…”

Section: B Inbetweeningmentioning

confidence: 99%

CONEA: CONsumer Editable Animation

Sean

Tian

2008

2008 5th IEEE Consumer Communications and Networking Conference

Self Cite

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Animation created using computer graphics is seen through a viewport which is dictated by what the director or producer wants the consumer to watch. This includes the usual cinematography to bring out the best dramatical effects. Consumers, however, do not have a choice but to passively receive the imagery that unfolds through the screen. This paper proposes the concept of a consumer editable animation, which aims to provide consumers with the flexibility to change attributes of the scene and also to decide what and how they want to view the final animation. Another key point is that all data are vector-based and the animation sequences are therefore rescalable.

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DBSC‐based animation enhanced with feature and motion

Cited by 22 publications

References 13 publications

Transductive graph based cartoon synthesis

Transductive graph based cartoon synthesis

Semi-automatic cartoon generation by motion planning

CONEA: CONsumer Editable Animation

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