Spatio-temporal Manifold Learning for Human Motions via Long-horizon Modeling

Wang, H orcid.org/0000-0002-2281-5679, Ho, ESL, Shum, HPH et al. (1 more author) (2021) Spatio-temporal Manifold Learning for Human Motions via Long-horizon Modeling. IEEE Transactions on Visualization and Computer Graphics, 27 (1). pp. 216-227. ISSN 1077-2626

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Copyright, Publisher and Additional Information: This article is protected by copyright. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords: Computer Graphics, Computer Animation, Character Animation, Deep Learning
Dates:
  • Published: January 2021
  • Accepted: 11 August 2019
  • Published (online): 22 August 2019
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds)
Funding Information:
FunderGrant number
EPSRC (Engineering and Physical Sciences Research Council)EP/R031193/1
Depositing User: Symplectic Publications
Date Deposited: 20 Aug 2019 09:51
Last Modified: 16 Dec 2020 13:17
Status: Published
Publisher: Institute of Electrical and Electronics Engineers
Identification Number: https://doi.org/10.1109/TVCG.2019.2936810

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