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Despite their potential, markerless hand tracking technologies are not yetapplied in practice to the diagnosis or monitoring of the activity ininflammatory musculoskeletal diseases. One reason is that the focus of mostmethods lies in the reconstruction of coarse, plausible poses, whereas in theclinical context, accurate, interpretable, and reliable results are required.Therefore, we propose ShaRPy, the first RGB-D Shape Reconstruction and handPose tracking system, which provides uncertainty estimates of the computedpose, e.g., when a finger is hidden or its estimate is inconsistent with theobservations in the input, to guide clinical decision-making. Besides pose,ShaRPy approximates a personalized hand shape, promoting a more realistic andintuitive understanding of its digital twin. Our method requires only alight-weight setup with a single consumer-level RGB-D camera yet it is able todistinguish similar poses with only small joint angle deviations in ametrically accurate space. This is achieved by combining a data-driven densecorrespondence predictor with traditional energy minimization. To bridge thegap between interactive visualization and biomedical simulation we leverage aparametric hand model in which we incorporate biomedical constraints andoptimize for both, its pose and hand shape. We evaluate ShaRPy on a keypointdetection benchmark and show qualitative results of hand function assessmentsfor activity monitoring of musculoskeletal diseases.

ShaRPy: Shape Reconstruction and Hand Pose Estimation from RGB-D with Uncertainty