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Combining motion matching and orientation prediction to animate avatars for consumer-grade VR devices
dc.contributor.author | Pontón Martínez, José Luis |
dc.contributor.author | Yun, Haoran |
dc.contributor.author | Andújar Gran, Carlos Antonio |
dc.contributor.author | Pelechano Gómez, Núria |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació |
dc.contributor.other | Universitat Politècnica de Catalunya. Doctorat en Computació |
dc.date.accessioned | 2023-03-21T09:05:32Z |
dc.date.available | 2023-03-21T09:05:32Z |
dc.date.issued | 2022-12 |
dc.identifier.citation | Ponton, J.L. [et al.]. Combining motion matching and orientation prediction to animate avatars for consumer-grade VR devices. "Computer graphics forum", Desembre 2022, vol. 41, núm. 8, p. 107-118. |
dc.identifier.issn | 1467-8659 |
dc.identifier.uri | http://hdl.handle.net/2117/385236 |
dc.description.abstract | The animation of user avatars plays a crucial role in conveying their pose, gestures, and relative distances to virtual objects or other users. Self-avatar animation in immersive VR helps improve the user experience and provides a Sense of Embodiment. However, consumer-grade VR devices typically include at most three trackers, one at the Head Mounted Display (HMD), and two at the handheld VR controllers. Since the problem of reconstructing the user pose from such sparse data is ill-defined, especially for the lower body, the approach adopted by most VR games consists of assuming the body orientation matches that of the HMD, and applying animation blending and time-warping from a reduced set of animations. Unfortunately, this approach produces noticeable mismatches between user and avatar movements. In this work we present a new approach to animate user avatars that is suitable for current mainstream VR devices. First, we use a neural network to estimate the user's body orientation based on the tracking information from the HMD and the hand controllers. Then we use this orientation together with the velocity and rotation of the HMD to build a feature vector that feeds a Motion Matching algorithm. We built a MoCap database with animations of VR users wearing a HMD and used it to test our approach on both self-avatars and other users’ avatars. Our results show that our system can provide a large variety of lower body animations while correctly matching the user orientation, which in turn allows us to represent not only forward movements but also stepping in any direction. |
dc.description.sponsorship | This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 860768 (CLIPE project) and the Spanish Ministry of Science and Innovation (PID2021-122136OB-C21). |
dc.format.extent | 12 p. |
dc.language.iso | eng |
dc.rights | Attribution-NonCommercial 4.0 International |
dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Infografia |
dc.subject.lcsh | Computer animation |
dc.subject.lcsh | Virtual reality |
dc.subject.lcsh | Avatars (Virtual reality) |
dc.subject.other | Self-avatars |
dc.subject.other | User models |
dc.subject.other | Motion Capture |
dc.title | Combining motion matching and orientation prediction to animate avatars for consumer-grade VR devices |
dc.type | Article |
dc.subject.lemac | Animació per ordinador |
dc.subject.lemac | Realitat virtual |
dc.subject.lemac | Avatars (Realitat virtual) |
dc.contributor.group | Universitat Politècnica de Catalunya. ViRVIG - Grup de Recerca en Visualització, Realitat Virtual i Interacció Gràfica |
dc.identifier.doi | 10.1111/cgf.14628 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://onlinelibrary.wiley.com/doi/10.1111/cgf.14628 |
dc.rights.access | Open Access |
local.identifier.drac | 35243514 |
dc.description.version | Postprint (published version) |
dc.relation.projectid | info:eu-repo/grantAgreement/EC/H2020/860768/EU/Creating Lively Interactive Populated Environments/CLIPE |
dc.relation.projectid | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2021-122136OB-C21/ES/Entornos 3D de alta fidelidad para Realidad Virtual y Computación Visual: geometría, movimiento, interacción y visualización para salud, arquitectura y ciudades/ |
local.citation.author | Ponton, J.L.; Yun, H.; Andujar, C.; Pelechano, N. |
local.citation.publicationName | Computer graphics forum |
local.citation.volume | 41 |
local.citation.number | 8 |
local.citation.startingPage | 107 |
local.citation.endingPage | 118 |
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