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Fast Terrain-Adaptive Motion Generation using Deep Neural Networks

Published: 17 November 2019 Publication History

Abstract

We propose a fast motion adaptation framework using deep neural networks. Traditionally, motion adaptation is performed via iterative numerical optimization. We adopted deep neural networks and replaced the iterative process with the feed-forward inference consisting of simple matrix multiplications. For efficient mapping from contact constraints to character motion, the proposed system is composed of two types of networks: trajectory and pose generators. The networks are trained using augmented motion capture data and are fine-tuned using the inverse kinematics loss. In experiments, our system successfully generates multi-contact motions of a hundred of characters in real-time, and the result motions contain the naturalness existing in the motion capture data.

References

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2018. OpenVINO™tookkit: Open Visual Inference and Neural network Optimization toolkit. https://01.org/openvinotoolkit/.
[2]
Daniel Holden, Ikhsanul Habibie, Ikuo Kusajima, and Taku Komura. 2017. Fast Neural Style Transfer for Motion Data. IEEE Computer Graphics and Applications 37, 4 (2017), 42–49.
[3]
Daniel Holden, Jun Saito, and Taku Komura. 2016. A Deep Learning Framework for Character Motion Synthesis and Editing. ACM Trans. Graph. 35, 4, Article 138 (July 2016), 11 pages.
[4]
T. Igarashi, T. Moscovich, and J. F. Hughes. 2005. Spatial Keyframing for Performance-driven Animation. In Proceedings of the 2005 ACM SIGGRAPH/Eurographics Symposium on Computer Animation(SCA ’05). ACM, New York, NY, USA, 107–115.
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Justin Johnson, Alexandre Alahi, and Li Fei-Fei. 2016. Perceptual losses for real-time style transfer and super-resolution. In European Conference on Computer Vision.
[6]
Changgu Kang and Sung-Hee Lee. 2017. Multi-Contact Locomotion Using a Contact Graph with Feasibility Predictors. ACM Trans. Graph. 36, 2, Article 145b (April 2017).
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Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver. 2016. Prioritized Experience Replay. In International Conference on Learning Representations. Puerto Rico.

Cited By

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  • (2025)Diverse Motion In-Betweening From Sparse Keyframes With Dual Posture StitchingIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.336345731:2(1402-1413)Online publication date: Feb-2025
  • (2021)A Survey on Deep Learning for Skeleton‐Based Human AnimationComputer Graphics Forum10.1111/cgf.1442641:1(122-157)Online publication date: 21-Nov-2021

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      cover image ACM Conferences
      SA '19: SIGGRAPH Asia 2019 Technical Briefs
      November 2019
      121 pages
      ISBN:9781450369459
      DOI:10.1145/3355088
      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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      New York, NY, United States

      Publication History

      Published: 17 November 2019

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      Author Tags

      1. Character Animation
      2. Deep Neural Networks
      3. Inverse Kinematics

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      • Research-article
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      SA '19
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      SA '19: SIGGRAPH Asia 2019
      November 17 - 20, 2019
      QLD, Brisbane, Australia

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      Overall Acceptance Rate 178 of 869 submissions, 20%

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      View all
      • (2025)Diverse Motion In-Betweening From Sparse Keyframes With Dual Posture StitchingIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.336345731:2(1402-1413)Online publication date: Feb-2025
      • (2021)A Survey on Deep Learning for Skeleton‐Based Human AnimationComputer Graphics Forum10.1111/cgf.1442641:1(122-157)Online publication date: 21-Nov-2021

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