Lightweight Quaternion Transition Generation with Neural Networks.

Romi Geleijn; Adrian Radziszewski; Julia Beryl van Straaten; Henrique Galvan Debarba

doi:10.1109/VRW52623.2021.00172

Lightweight Quaternion Transition Generation with Neural Networks.

Romi Geleijn, Adrian Radziszewski, Julia Beryl van Straaten, Henrique Galvan Debarba

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

2 Citations (Scopus)

Abstract

This paper introduces the Quaternion Transition Generator (QTG), a new network architecture tailored to animation transition generation for virtual characters. The QTG is simpler than the current state of the art, making it lightweight and easier to implement. It uses approximately 80% fewer arithmetic operations compared to other transition networks. Additionally, this architecture is capable of generating visually accurate rotation-based animations transitions and results in a lower Mean Absolute Error than transition generation techniques that are commonly used for animation blending.

Original language	Undefined/Unknown
Title of host publication	2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)
Number of pages	2
Publisher	IEEE
Publication date	2021
Pages	579-580
Article number	9419271
ISBN (Print)	978-1-6654-1166-0
ISBN (Electronic)	978-1-6654-4057-8
DOIs	https://doi.org/10.1109/VRW52623.2021.00172
Publication status	Published - 2021
Externally published	Yes
Event	2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2021 - Virtual, Lisbon, Portugal Duration: 27 Mar 2021 → 3 Apr 2021

Conference

Conference	2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2021
Country/Territory	Portugal
City	Virtual, Lisbon
Period	27/03/2021 → 03/04/2021

Bibliographical note

DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

Access to Document

10.1109/VRW52623.2021.00172

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Cite this

@inproceedings{f0e6e3cbe6f3433abda4f3a11a82f29c,

title = "Lightweight Quaternion Transition Generation with Neural Networks.",

abstract = "This paper introduces the Quaternion Transition Generator (QTG), a new network architecture tailored to animation transition generation for virtual characters. The QTG is simpler than the current state of the art, making it lightweight and easier to implement. It uses approximately 80% fewer arithmetic operations compared to other transition networks. Additionally, this architecture is capable of generating visually accurate rotation-based animations transitions and results in a lower Mean Absolute Error than transition generation techniques that are commonly used for animation blending.",

author = "Romi Geleijn and Adrian Radziszewski and Straaten, {Julia Beryl van} and Debarba, {Henrique Galvan}",

note = "DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.; 2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2021 ; Conference date: 27-03-2021 Through 03-04-2021",

year = "2021",

doi = "10.1109/VRW52623.2021.00172",

language = "Udefineret/Ukendt",

isbn = "978-1-6654-1166-0",

pages = "579--580",

booktitle = "2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)",

publisher = "IEEE",

address = "USA",

}

Geleijn, R, Radziszewski, A, Straaten, JBV & Debarba, HG 2021, Lightweight Quaternion Transition Generation with Neural Networks. in 2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)., 9419271, IEEE, pp. 579-580, 2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2021, Virtual, Lisbon, Portugal, 27/03/2021. https://doi.org/10.1109/VRW52623.2021.00172

Lightweight Quaternion Transition Generation with Neural Networks. / Geleijn, Romi; Radziszewski, Adrian; Straaten, Julia Beryl van et al.
2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). IEEE, 2021. p. 579-580 9419271.

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

TY - GEN

T1 - Lightweight Quaternion Transition Generation with Neural Networks.

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AU - Radziszewski, Adrian

AU - Straaten, Julia Beryl van

AU - Debarba, Henrique Galvan

N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

PY - 2021

Y1 - 2021

N2 - This paper introduces the Quaternion Transition Generator (QTG), a new network architecture tailored to animation transition generation for virtual characters. The QTG is simpler than the current state of the art, making it lightweight and easier to implement. It uses approximately 80% fewer arithmetic operations compared to other transition networks. Additionally, this architecture is capable of generating visually accurate rotation-based animations transitions and results in a lower Mean Absolute Error than transition generation techniques that are commonly used for animation blending.

AB - This paper introduces the Quaternion Transition Generator (QTG), a new network architecture tailored to animation transition generation for virtual characters. The QTG is simpler than the current state of the art, making it lightweight and easier to implement. It uses approximately 80% fewer arithmetic operations compared to other transition networks. Additionally, this architecture is capable of generating visually accurate rotation-based animations transitions and results in a lower Mean Absolute Error than transition generation techniques that are commonly used for animation blending.

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DO - 10.1109/VRW52623.2021.00172

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