article

Interactive motion generation from examples

Authors:

D. A. ForsythAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 21, Issue 3

Pages 483 - 490

https://doi.org/10.1145/566654.566606

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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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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Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 21, Issue 3

July 2002

548 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/566654

Issue’s Table of Contents

Copyright © 2002 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 July 2002

Published in TOG Volume 21, Issue 3

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Xu RTran VNayar SKrishnan G(2024)DanceCraft: A Music-Reactive Real-time Dance Improv SystemProceedings of the 9th International Conference on Movement and Computing10.1145/3658852.3659078(1-10)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3658852.3659078
Starke SStarke PHe NKomura TYe Y(2024)Categorical Codebook Matching for Embodied Character ControllersACM Transactions on Graphics10.1145/365820943:4(1-14)Online publication date: 19-Jul-2024
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Li PStarke SYe YSorkine-Hornung O(2024)WalkTheDog: Cross-Morphology Motion Alignment via Phase ManifoldsACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657508(1-10)Online publication date: 13-Jul-2024
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Cohan STevet GReda DPeng Xvan de Panne M(2024)Flexible Motion In-betweening with Diffusion ModelsACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657414(1-9)Online publication date: 13-Jul-2024
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Rekik RWuhrer SHoyet LZibrek KOlivier A(2024)A Survey on Realistic Virtual Human Animations: Definitions, Features and EvaluationsComputer Graphics Forum10.1111/cgf.1506443:2Online publication date: 30-Apr-2024
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Zhu WMa XRo DCi HZhang JShi JGao FTian QWang Y(2024)Human Motion Generation: A SurveyIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2023.333093546:4(2430-2449)Online publication date: 1-Apr-2024
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Bai YGeng XMangalam KBar AYuille ADarrell TMalik JEfros A(2024)Sequential Modeling Enables Scalable Learning for Large Vision Models2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.02157(22861-22872)Online publication date: 16-Jun-2024
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