ShaRPy: Shape Reconstruction and Hand Pose Estimation from RGB-D with Uncertainty
Wirth V, Liphardt AM, Coppers B, Bräunig J, Heinrich S, Leyendecker S, Kleyer A, Schett G, Vossiek M, Egger B, Stamminger M (2023)
Publication Language: English
Publication Type: Conference contribution, Conference Contribution
Publication year: 2023
Pages Range: 2625-2633
Conference Proceedings Title: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops
Event location: Paris
Open Access Link: https://openaccess.thecvf.com/content/ICCV2023W/CVAMD/html/Wirth_ShaRPy_Shape_Reconstruction_and_Hand_Pose_Estimation_from_RGB-D_with_ICCVW_2023_paper.html
Abstract
Despite their potential, markerless hand tracking technologies are not yet applied in practice to the diagnosis or monitoring of the activity in inflammatory musculoskeletal diseases. One reason is that the focus of most methods lies in the reconstruction of coarse, plausible poses, whereas in the clinical context, accurate, interpretable, and reliable results are required. Therefore, we propose ShaRPy, the first RGB-D Shape Reconstruction and hand Pose tracking system, which provides uncertainty estimates of the computed pose, e.g., when a finger is hidden or its estimate is inconsistent with the observations in the input, to guide clinical decision-making. Besides pose, ShaRPy approximates a personalized hand shape, promoting a more realistic and intuitive understanding of its digital twin. Our method requires only a light-weight setup with a single consumer-level RGB-D camera yet it is able to distinguish similar poses with only small joint angle deviations in a metrically accurate space. This is achieved by combining a data-driven dense correspondence predictor with traditional energy minimization. To bridge the gap between interactive visualization and biomedical simulation we leverage a parametric hand model in which we incorporate biomedical constraints and optimize for both, its pose and hand shape parameters. We evaluate ShaRPy on a keypoint detection benchmark and show qualitative results of hand function assessments for activity monitoring of musculoskeletal diseases.
Authors with CRIS profile
Vanessa Wirth
Chair of Visual Computing (LGDV)
Anna-Maria Liphardt
Department of Medicine 3 – Rheumatology and Immunology
Birte Coppers
Medizinische Fakultät
Johanna Bräunig
Lehrstuhl für Hochfrequenztechnik (LHFT)
Simon Heinrich
Institute of Applied Dynamics
Sigrid Leyendecker
Institute of Applied Dynamics
Arnd Kleyer
Department of Medicine 3 – Rheumatology and Immunology
Georg Schett
Lehrstuhl für Innere Medizin III
Martin Vossiek
Lehrstuhl für Hochfrequenztechnik (LHFT)
Bernhard Egger
Professur für Cognitive Computer Vision (Stiftungsprofessur)
Marc Stamminger
Chair of Visual Computing (LGDV)
Additional Organisation(s)
Sonderforschungsbereich 1483/1 Empathokinästhetische Sensorik – Sensortechniken und Datenanalyseverfahren zur empathokinästhetischen Modellbildung und Zustandsbestimmung (EmpkinS)
FAU Profilzentrum Licht.Materie.Quantentechnologien (FAU LMQ)
Related research project(s)
How to cite
APA:
Wirth, V., Liphardt, A.-M., Coppers, B., Bräunig, J., Heinrich, S., Leyendecker, S.,... Stamminger, M. (2023). ShaRPy: Shape Reconstruction and Hand Pose Estimation from RGB-D with Uncertainty. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops (pp. 2625-2633). Paris, FR.
MLA:
Wirth, Vanessa, et al. "ShaRPy: Shape Reconstruction and Hand Pose Estimation from RGB-D with Uncertainty." Proceedings of the International Conference on Computer Vision (ICCV) Workshops, Paris 2023. 2625-2633.
BibTeX: Download