BodySLAM: Opportunistic User Digitization in Multi-User AR/VR Experiences @FiglabCMU
BodySLAM: Opportunistic User Digitization in Multi-User AR/VR Experiences  @FiglabCMU
Uploaded October 2020 | Updated September 2026, 2 hours ago
Today’s augmented and virtual reality (AR/VR) systems do not provide body, hand or mouth tracking without special worn sensors or external infrastructure. Simultaneously, AR/VR systems are increasingly being used in co-located, multi-user experiences, opening the possibility for opportunistic capture of other users. This is the core idea behind BodySLAM, which uses disparate camera views from users to digitize the body, hands and mouth of other people, and then relay that information back to the respective users. If a user is seen by two or more people, 3D pose can be estimated via stereo reconstruction. Our system also maps the arrangement of users in real-world coordinates. Our approach requires no additional hardware or sensors beyond what is already found in commercial AR/VR devices, such as Microsoft HoloLens or Oculus Quest.

For further details visit: karan-ahuja.com/bodyslam.html

Karan Ahuja, Mayank Goel, and Chris Harrison. 2020. BodySLAM: Opportunistic User Digitization in Multi-User AR/VR Experiences. In Symposium on Spatial User Interaction (SUI '20). Association for Computing Machinery, New York, NY, USA, Article 16, 1–8. DOI:doi.org/10.1145/3385959.3418452
BodySLAM: Opportunistic User Digitization in Multi-User AR/VR ExperiencesPatternTrack: Multi-Device Tracking Using Infrared, Structured-Light Projections from Built-in LiDARHiFiGaze: Improving Eye Tracking Accuracy Using Screen Content KnowledgeSkinTrack (Yang Zhang - ACM CHI 2016)Fluid Reality (ACM UIST 2023 Talk)Vid2Doppler: Synthesizing Doppler Radar Data from Videos for Privacy-Preserving Activity RecognitionSuper-Resolution Capacitive TouchscreensDIRECT: Touch Tracking on Ordinary Surfaces with Hybrid Depth-Infrared Sensing (ACM ISS 2016)EclipseTouch: Touch Segmentation on Ad Hoc Surfaces using Worn Infrared Shadow CastingSozu: Self-Powered Radio Tags for Building-Scale Activity SensingUIST 2017 Student Innovation Contest: Robotic ArmPantœnna: Mouth Pose Estimation for VR/AR Headsets Using Low-Profile Antenna
Future Interfaces Group |

BodySLAM: Opportunistic User Digitization in Multi-User AR/VR Experiences

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