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Motion In-betweening for Physically Simulated Characters

Published: 13 December 2022 Publication History

Abstract

We present a motion in-betweening framework to generate high quality, physically plausible character animation when we are given temporally sparse keyframes as soft animation constraints. More specifically, we learn imitation policies for physically simulated characters by using deep reinforcement learning where the policies can access limited information only. Once learned, the physically simulated characters are capable of adapting to external perturbations while following given sparse input keyframes. We demonstrate the performance of our framework on two different motion datasets and also compare our results with the the results generated by a baseline imitation policy.

Supplementary Material

Supplemental video, Poster PDF (Inbetweening_SIGGRAPH_Asia.pdf)
MP4 File (SIG_Asia_Supplemental.mp4)
Supplemental video, Poster PDF

References

[1]
Félix G. Harvey, Mike Yurick, Derek Nowrouzezahrai, and Christopher Pal. 2020. Robust Motion In-Betweening. 39, 4 (2020).
[2]
Daniel Holden, Taku Komura, and Jun Saito. 2017. Phase-functioned Neural Networks for Character Control. ACM Trans. Graph. 36, 4, Article 42 (July 2017). http://doi.acm.org/10.1145/3072959.3073663
[3]
Jungdam Won, Deepak Gopinath, and Jessica Hodgins. 2020. A Scalable Approach to Control Diverse Behaviors for Physically Simulated Characters. ACM Trans. Graph. 39, 4, Article 33 (2020). https://doi.org/10.1145/3386569.3392381

Cited By

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  • (2024)MaskedMimic: Unified Physics-Based Character Control Through Masked Motion InpaintingACM Transactions on Graphics10.1145/368795143:6(1-21)Online publication date: 19-Nov-2024
  • (2024)PIMT: Physics-Based Interactive Motion Transition for Hybrid Character AnimationProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681582(10497-10505)Online publication date: 28-Oct-2024
  • (2023)Motion In-Betweening with Phase ManifoldsProceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/36069216:3(1-17)Online publication date: 24-Aug-2023

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  1. Motion In-betweening for Physically Simulated Characters

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      Published In

      cover image ACM Conferences
      SA '22: SIGGRAPH Asia 2022 Posters
      December 2022
      120 pages
      ISBN:9781450394628
      DOI:10.1145/3550082
      • Editors:
      • Soon Ki Jung,
      • Neil Dodgson
      Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 13 December 2022

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      Author Tags

      1. Character Animation
      2. Deep Learning
      3. Multi-agent
      4. Neural Network
      5. Physics-based Simulation and Control
      6. Reinforcement Learning

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      • Poster
      • Research
      • Refereed limited

      Conference

      SA '22
      Sponsor:
      SA '22: SIGGRAPH Asia 2022
      December 6 - 9, 2022
      Daegu, Republic of Korea

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      Overall Acceptance Rate 178 of 869 submissions, 20%

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      Cited By

      View all
      • (2024)MaskedMimic: Unified Physics-Based Character Control Through Masked Motion InpaintingACM Transactions on Graphics10.1145/368795143:6(1-21)Online publication date: 19-Nov-2024
      • (2024)PIMT: Physics-Based Interactive Motion Transition for Hybrid Character AnimationProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681582(10497-10505)Online publication date: 28-Oct-2024
      • (2023)Motion In-Betweening with Phase ManifoldsProceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/36069216:3(1-17)Online publication date: 24-Aug-2023

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