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

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

3 Citationer (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.

Originalsprog	Udefineret/Ukendt
Titel	2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)
Antal sider	2
Forlag	IEEE (Institute of Electrical and Electronics Engineers)
Publikationsdato	2021
Sider	579-580
Artikelnummer	9419271
ISBN (Trykt)	978-1-6654-1166-0
ISBN (Elektronisk)	978-1-6654-4057-8
DOI	https://doi.org/10.1109/VRW52623.2021.00172
Status	Udgivet - 2021
Udgivet eksternt	Ja
Begivenhed	2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2021 - Virtual, Lisbon, Portugal Varighed: 27 mar. 2021 → 3 apr. 2021

Konference

Konference	2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2021
Land/Område	Portugal
By	Virtual, Lisbon
Periode	27/03/2021 → 03/04/2021

Adgang til dokumentet

10.1109/VRW52623.2021.00172

AUB Link

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Citationsformater

@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 (Institute of Electrical and Electronics Engineers)",

address = "USA",

}

Geleijn, R, Radziszewski, A, Straaten, JBV & Debarba, HG 2021, Lightweight Quaternion Transition Generation with Neural Networks. i 2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)., 9419271, IEEE (Institute of Electrical and Electronics Engineers), s. 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 (Institute of Electrical and Electronics Engineers), 2021. s. 579-580 9419271.

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

TY - GEN

T1 - Lightweight Quaternion Transition Generation with Neural Networks.

AU - Geleijn, Romi

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.

U2 - 10.1109/VRW52623.2021.00172

DO - 10.1109/VRW52623.2021.00172

M3 - Konferenceartikel i proceeding

SN - 978-1-6654-1166-0

SP - 579

EP - 580

BT - 2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)

PB - IEEE (Institute of Electrical and Electronics Engineers)

T2 - 2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2021

Y2 - 27 March 2021 through 3 April 2021

ER -