NNWarp: Neural Network-based Nonlinear Deformation

Luo, R, Shao, T, Wang, H et al. (4 more authors) (2020) NNWarp: Neural Network-based Nonlinear Deformation. IEEE Transactions on Visualization and Computer Graphics, 26 (4). pp. 1745-1759. ISSN 1077-2626

Abstract

Metadata

Authors/Creators:
  • Luo, R
  • Shao, T
  • Wang, H
  • Xu, W
  • Chen, X
  • Zhou, K
  • Yang, Y
Copyright, Publisher and Additional Information: © 2018 IEEE. This is an author produced version of a paper published in IEEE Transactions on Visualization and Computer Graphics. 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. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: neural network , machine learning , data-driven animation , nonlinear regression , deformable model , physics-based simulation
Dates:
  • Published: 1 April 2020
  • Accepted: 29 October 2018
  • Published (online): 15 November 2018
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds)
The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 11 Jan 2019 16:02
Last Modified: 30 Jun 2020 14:49
Status: Published
Publisher: Institute of Electrical and Electronics Engineers
Identification Number: https://doi.org/10.1109/TVCG.2018.2881451

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