Article

Interactive motion generation from examples

Authors:

D. A. ForsythAuthors Info & Claims

SIGGRAPH '02: Proceedings of the 29th annual conference on Computer graphics and interactive techniques

Pages 483 - 490

https://doi.org/10.1145/566570.566606

Published: 01 July 2002 Publication History

Abstract

There are many applications that demand large quantities of natural looking motion. It is difficult to synthesize motion that looks natural, particularly when it is people who must move. In this paper, we present a framework that generates human motions by cutting and pasting motion capture data. Selecting a collection of clips that yields an acceptable motion is a combinatorial problem that we manage as a randomized search of a hierarchy of graphs. This approach can generate motion sequences that satisfy a variety of constraints automatically. The motions are smooth and human-looking. They are generated in real time so that we can author complex motions interactively. The algorithm generates multiple motions that satisfy a given set of constraints, allowing a variety of choices for the animator. It can easily synthesize multiple motions that interact with each other using constraints. This framework allows the extensive re-use of motion capture data for new purposes.

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Cited By

Kucherenko TWolfert PYoon YViegas CNikolov TTsakov MHenter G(2024)Evaluating Gesture Generation in a Large-scale Open Challenge: The GENEA Challenge 2022ACM Transactions on Graphics10.1145/365637443:3(1-28)Online publication date: 23-Jun-2024
https://doi.org/10.1145/3656374
Rastgoo RKiani KEscalera SAthitsos VSabokrou M(2024)A survey on recent advances in Sign Language ProductionExpert Systems with Applications10.1016/j.eswa.2023.122846243(122846)Online publication date: Jun-2024
https://doi.org/10.1016/j.eswa.2023.122846
Kovar LGleicher MPighin F(2023)Motion GraphsSeminal Graphics Papers: Pushing the Boundaries, Volume 210.1145/3596711.3596788(723-732)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1145/3596711.3596788
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Index Terms

Interactive motion generation from examples
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Discrete space search
      2. Game tree search
  2. Computer graphics
    1. Animation

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cover image ACM Conferences

SIGGRAPH '02: Proceedings of the 29th annual conference on Computer graphics and interactive techniques

July 2002

574 pages

ISBN:1581135211

DOI:10.1145/566570

Conference Chair:
Tom Appolloni
Harris Corporation

Copyright © 2002 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

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Published: 01 July 2002

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SIGGRAPH02: The 29th International Conference on Computer Graphics and Interactive Techniques

July 23 - 26, 2002

Texas, San Antonio

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SIGGRAPH '02 Paper Acceptance Rate 67 of 358 submissions, 19%;

Overall Acceptance Rate 1,822 of 8,601 submissions, 21%

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Cited By

Kucherenko TWolfert PYoon YViegas CNikolov TTsakov MHenter G(2024)Evaluating Gesture Generation in a Large-scale Open Challenge: The GENEA Challenge 2022ACM Transactions on Graphics10.1145/365637443:3(1-28)Online publication date: 23-Jun-2024
https://doi.org/10.1145/3656374
Rastgoo RKiani KEscalera SAthitsos VSabokrou M(2024)A survey on recent advances in Sign Language ProductionExpert Systems with Applications10.1016/j.eswa.2023.122846243(122846)Online publication date: Jun-2024
https://doi.org/10.1016/j.eswa.2023.122846
Kovar LGleicher MPighin F(2023)Motion GraphsSeminal Graphics Papers: Pushing the Boundaries, Volume 210.1145/3596711.3596788(723-732)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1145/3596711.3596788
Kim CEom HYoo JChoi SNoh J(2023)Interactive Locomotion Style Control for a Human Character based on Gait Cycle FeaturesComputer Graphics Forum10.1111/cgf.1498843:1Online publication date: 18-Oct-2023
https://doi.org/10.1111/cgf.14988
Nyatsanga SKucherenko TAhuja CHenter GNeff M(2023)A Comprehensive Review of Data‐Driven Co‐Speech Gesture GenerationComputer Graphics Forum10.1111/cgf.1477642:2(569-596)Online publication date: 23-May-2023
https://doi.org/10.1111/cgf.14776
Häfliger AKurabayashi S(2022)Dynamic Motion Matching: Design and Implementation of a Context-Aware Animation System for GamesInternational Journal of Semantic Computing10.1142/S1793351X2240008616:02(189-212)Online publication date: 28-May-2022
https://doi.org/10.1142/S1793351X22400086
Maheshwari SGupta DSarvadevabhatla R(2022)MUGL: Large Scale Multi Person Conditional Action Generation with Locomotion2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV51458.2022.00082(747-755)Online publication date: Jan-2022
https://doi.org/10.1109/WACV51458.2022.00082
Wang HDu DLi JJi WYu L(2021)A Cyclic Consistency Motion Style Transfer Method Combined with Kinematic ConstraintsJournal of Sensors10.1155/2021/55486142021(1-17)Online publication date: 29-Jun-2021
https://doi.org/10.1155/2021/5548614
Hafliger AKurabayashi S(2021)Dynamic Motion Matching: Context-Aware Character Animation with Subspaces Ensembling2021 IEEE International Symposium on Multimedia (ISM)10.1109/ISM52913.2021.00028(115-122)Online publication date: Nov-2021
https://doi.org/10.1109/ISM52913.2021.00028
Rastgoo RKiani KEscalera SSabokrou M(2021)Sign Language Production: A Review2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW53098.2021.00384(3446-3456)Online publication date: Jun-2021
https://doi.org/10.1109/CVPRW53098.2021.00384
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