abstract

CasualVRVideos: VR videos from casual stationary videos

Authors:

Stefanie Zollmann,

Anthony Dickson,

Jonathan VenturaAuthors Info & Claims

VRST '20: Proceedings of the 26th ACM Symposium on Virtual Reality Software and Technology

Article No.: 48, Pages 1 - 3

https://doi.org/10.1145/3385956.3422119

Published: 01 November 2020 Publication History

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Abstract

Thanks to the ubiquity of devices capable of recording and playing back video, the amount of video files is growing at a rapid rate. Most of us have now video recordings of major events in our lives. However, until today, these videos are captured mainly in 2D and are mostly used for screen-based video replay. Currently there is no way for watching them in more immersive environments such as on a VR headset. They are simply not optimized for playback in stereoscopic displays or even tracked Virtual Reality devices.

In this work, we present CasualVRVideos, a first approach that works towards solving these issues by extracting spatial information from video footage recorded in 2D, so that it can later be played back in VR displays to increase the immersion. We focus in particular on the challenging scenario when the camera itself is not moving.

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References

[1]

L. Baker, S. Mills, S. Zollmann, and J. Ventura. 2020. CasualStereo: Casual Capture of Stereo Panoramas with Spherical Structure-from-Motion. In 2020 IEEE Conference on Virtual Reality and 3D User Interfaces (VR). 782–790.

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Michael Broxton, John Flynn, Ryan Overbeck, Daniel Erickson, Peter Hedman, Matthew DuVall, Jason Dourgarian, Jay Busch, Matt Whalen, and Paul Debevec. 2020. Immersive Light Field Video with a Layered Mesh Representation. 39, 4 (2020), 86:1–86:15.

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Dickson AShanks JVentura JKnott AZollmann S(2022)VRVideos: A flexible pipeline for Virtual Reality Video Creation2022 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)10.1109/AIVR56993.2022.00039(199-202)Online publication date: Dec-2022
https://doi.org/10.1109/AIVR56993.2022.00039
Dickson AKnott AZollmann S(2021)Benchmarking Monocular Depth Estimation Models for VR Content Creation from a User Perspective2021 36th International Conference on Image and Vision Computing New Zealand (IVCNZ)10.1109/IVCNZ54163.2021.9653344(1-6)Online publication date: 9-Dec-2021
https://doi.org/10.1109/IVCNZ54163.2021.9653344

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Published In

VRST '20: Proceedings of the 26th ACM Symposium on Virtual Reality Software and Technology

November 2020

429 pages

ISBN:9781450376198

DOI:10.1145/3385956

Editors:
Robert J. Teather
Carleton University, Canada
,
Chris Joslin
Carleton University, Canada
,
Wolfgang Stuerzlinger
Simon Fraser University, Canada
,
Pablo Figueroa
Universidad de los Andes, Colombia
,
Yaoping Hu
University of Calgary, Canada
,
Anil Ufuk Batmaz
Simon Fraser University, Canada
,
Wonsook Lee
University of Ottawa, Canada
,
Francisco Ortega
Colorado State University, USA

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 01 November 2020

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New Zealand Marsden Council

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VRST '20

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VRST '20: 26th ACM Symposium on Virtual Reality Software and Technology

November 1 - 4, 2020

Virtual Event, Canada

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Overall Acceptance Rate 66 of 254 submissions, 26%

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Dickson AShanks JVentura JKnott AZollmann S(2022)VRVideos: A flexible pipeline for Virtual Reality Video Creation2022 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)10.1109/AIVR56993.2022.00039(199-202)Online publication date: Dec-2022
https://doi.org/10.1109/AIVR56993.2022.00039
Dickson AKnott AZollmann S(2021)Benchmarking Monocular Depth Estimation Models for VR Content Creation from a User Perspective2021 36th International Conference on Image and Vision Computing New Zealand (IVCNZ)10.1109/IVCNZ54163.2021.9653344(1-6)Online publication date: 9-Dec-2021
https://doi.org/10.1109/IVCNZ54163.2021.9653344

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