eprintid: 10120191
rev_number: 16
eprint_status: archive
userid: 608
dir: disk0/10/12/01/91
datestamp: 2021-02-08 16:33:27
lastmod: 2021-10-11 22:37:26
status_changed: 2021-02-08 16:33:27
type: proceedings_section
metadata_visibility: show
creators_name: Gonzalez-Franco, M
creators_name: Egan, Z
creators_name: Peachey, M
creators_name: Antley, A
creators_name: Randhavane, T
creators_name: Panda, P
creators_name: Zhang, Y
creators_name: Wang, CY
creators_name: Reilly, DF
creators_name: Peck, TC
creators_name: Won, AS
creators_name: Steed, A
creators_name: Ofek, E
title: MoveBox: Democratizing MoCap for the Microsoft Rocketbox Avatar Library
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F48
keywords: Avatars, Animation, Motion Capture, MoCap, Rigging, Depth Cameras, Lip Sync
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
abstract: This paper presents MoveBox an open sourced toolbox for animating motion captured (MoCap) movements onto the Microsoft Rocketbox library of avatars. Motion capture is performed using a single depth sensor, such as Azure Kinect or Windows Kinect V2. Motion capture is performed in real-time using a single depth sensor, such as Azure Kinect or Windows Kinect V2, or extracted from existing RGB videos offline leveraging deep-learning computer vision techniques. Our toolbox enables real-time animation of the user’s avatar by converting the transformations between systems that have different joints and hierarchies. Additional features of the toolbox include recording, playback and looping animations, as well as basic audio lip sync, blinking and resizing of avatars as well as finger and hand animations. Our main contribution is both in the creation of this open source tool as well as the validation on different devices and discussion of MoveBox’s capabilities by end users.
date: 2021
date_type: published
publisher: Institute of Electrical and Electronics Engineers (IEEE)
official_url: https://doi.org/10.1109/AIVR50618.2020.00026
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1844117
doi: 10.1109/AIVR50618.2020.00026
isbn_13: 978-1-7281-7463-1
lyricists_name: Steed, Anthony
lyricists_id: ASTEE91
actors_name: Steed, Anthony
actors_id: ASTEE91
actors_role: owner
full_text_status: public
publication: Proceedings - 2020 IEEE International Conference on Artificial Intelligence and Virtual Reality, AIVR 2020
place_of_pub: Utrecht, Netherlands
pagerange: 91-98
event_title: 2020 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)
institution: 2020 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)
book_title: Proceedings of the 2020 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)
citation:        Gonzalez-Franco, M;    Egan, Z;    Peachey, M;    Antley, A;    Randhavane, T;    Panda, P;    Zhang, Y;                         ... Ofek, E; + view all <#>        Gonzalez-Franco, M;  Egan, Z;  Peachey, M;  Antley, A;  Randhavane, T;  Panda, P;  Zhang, Y;  Wang, CY;  Reilly, DF;  Peck, TC;  Won, AS;  Steed, A;  Ofek, E;   - view fewer <#>    (2021)    MoveBox: Democratizing MoCap for the Microsoft Rocketbox Avatar Library.                     In:  Proceedings of the 2020 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR).  (pp. pp. 91-98).  Institute of Electrical and Electronics Engineers (IEEE): Utrecht, Netherlands.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10120191/1/MoveBox-Democratizing-MoCap-for-the-Microsoft-Rocketbox-Avatar-Library.pdf