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Cited by 1,534

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Synthesis and evaluation of linear motion transitions

Wang, Jing, Bodenheimer, Bobby·2008·ACM Transactions on Graphics

Abstract

This article develops methods for determining visually appealing motion transitions using linear blending. Motion transitions are segues between two sequences of animation, and are important components for generating compelling animation streams in virtual environments and computer games. Methods involving linear blending are studied because of their efficiency, computational speed, and widespread use. Two methods of transition specification are detailed, center-aligned and start-end transitions. First, we compute a set of optimal weights for an underlying cost metric used to determine the transition points. We then evaluate the optimally weighted cost metric for generalizability, appeal, and robustness through a cross-validation and user study. Next, we develop methods for computing visually appealing blend lengths for two broad categories of motion. We empirically evaluate these results through user studies. Finally, we assess the importance of these techniques by determining the minimum sensitivity of viewers to transition durations, the just noticeable difference, for both center-aligned and start-end specifications.

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Metadata

Authors
Wang, J., & Bodenheimer, B.
Venue
ACM Transactions on Graphics
Published
2008-03
Volume
27
Issue
1
Pages
1-15
ISSN
  • 0730-0301 (ISSN-L)
  • 1557-7368
OpenAlex
W2090267299
Cited by
1,534
References
0
FWCI
343.03
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Synthesis and evaluation of linear motion transitions. https://app.cochat.ai/papers/10.1145/1330511.1330512
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Wang, J., & Bodenheimer, B. (2008). Synthesis and evaluation of linear motion transitions. ACM Transactions on Graphics. https://doi.org/10.1145/1330511.1330512

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.