Faculty profile
Helge Rhodin
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Read how they describe their research on their University of British Columbia profile.
Latest papers
Outdoor Motion Capture at Scale.
Sensors (Basel, Switzerland) · 2026
A graph-based approach can improve keypoint detection of complex poses: a proof-of-concept on injury occurrences in alpine ski racing.
Scientific reports · 2023
Standardized 3D test object for multi-camera calibration during animal pose capture.
Neurophotonics · 2023
Latest funding
- $450,000
Care Anywhere: Smart Biosensors to Promote Healthy Aging and Transform Healthcare
NSERC · 2023 · Co-investigator
- $89,938
High Accuracy Non-Optical Motion Capture
NSERC · 2021 · Co-investigator
- $12,500
Mobile Motion Capture: From Photorealistic Avatars to Privacy Protection
NSERC · 2020 · Principal investigator
9 publications.
Outdoor Motion Capture at Scale.
Zwölfer M, Mössner M, Rhodin H, Nachbauer W
A graph-based approach can improve keypoint detection of complex poses: a proof-of-concept on injury occurrences in alpine ski racing.
Zwölfer M, Heinrich D, Wandt B, Rhodin H, Spörri J, Nachbauer W
Standardized 3D test object for multi-camera calibration during animal pose capture.
Hu H, Zhang R, Fong T, Rhodin H, Murphy TH
Deep learning-based 2D keypoint detection in alpine ski racing - A performance analysis of state-of-the-art algorithms applied to regular skiing and injury situations.
Zwölfer M, Heinrich D, Schindelwig K, Wandt B, Rhodin H, Spörri J, Nachbauer W
Towards a Visualizable, De-identified Synthetic Biomarker of Human Movement Disorders.
Hu H, Xiao D, Rhodin H, Murphy TH
LiftPose3D, a deep learning-based approach for transforming two-dimensional to three-dimensional poses in laboratory animals.
Gosztolai A, Günel S, Lobato-Ríos V, Pietro Abrate M, Morales D, Rhodin H, Fua P, Ramdya P
A three-dimensional virtual mouse generates synthetic training data for behavioral analysis.
Bolaños LA, Xiao D, Ford NL, LeDue JM, Gupta PK, Doebeli C, Hu H, Rhodin H, Murphy TH
Are Existing Monocular Computer Vision-Based 3D Motion Capture Approaches Ready for Deployment? A Methodological Study on the Example of Alpine Skiing.
Ostrek M, Rhodin H, Fua P, Müller E, Spörri J
DeepFly3D, a deep learning-based approach for 3D limb and appendage tracking in tethered, adult Drosophila.
Günel S, Rhodin H, Morales D, Campagnolo J, Ramdya P, Fua P
Care Anywhere: Smart Biosensors to Promote Healthy Aging and Transform Healthcare
Principal investigators: Hodgson, Antony J
Keywords: aging; bioengineering; biosensors; digital health; health technologies; machine learning; remote monitoring; wearables
High Accuracy Non-Optical Motion Capture
Principal investigators: Pai, Dinesh
Mobile Motion Capture: From Photorealistic Avatars to Privacy Protection
Principal investigators: Rhodin, Helge
Mobile Motion Capture: From Photorealistic Avatars to Privacy Protection
Principal investigators: Rhodin, Helge
Keywords: computer vision; mobile motion capture; augmented and virtual reality; virtual avatars and telepresence; neural rendering; egocentric vision; self-supervised machine learning; geometry-aware representation learning; interactive character control and animation; privacy
AuMoCap: Augmented, Portable, Real-Time, Markerless Motion Capture for Digital Humans
Principal investigators: Rhodin, Helge
From CIHR, NSERC and SSHRC funding decisions: CIHR since 2008, NSERC since 1991 and SSHRC since 1998, including their latest published competition results.
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Profile data last refreshed on September 27, 2026 from the university directory, publication records and CIHR, NSERC and SSHRC funding.