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

arXiv: https://arxiv.org/abs/2303.10042 · HTML: https://mlsanity.com/paper/2303.10042/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 34 references that are in the map.
- First posted on arXiv 17 Mar 2023.

## Builds on (most cited first, 20 of 20)

- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [YOLACT: Real-time Instance Segmentation](https://mlsanity.com/paper/1904.02689/) (2019)
- [Modular Primitives for High-Performance Differentiable Rendering](https://mlsanity.com/paper/2011.03277/) (2020)
- [End-to-End Human Pose and Mesh Reconstruction with Transformers](https://mlsanity.com/paper/2012.09760/) (2020)
- [H2O: Two Hands Manipulating Objects for First Person Interaction Recognition](https://mlsanity.com/paper/2104.11181/) (2021)
- [InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose Estimation from a Single RGB Image](https://mlsanity.com/paper/2008.09309/) (2020)
- [Learning to Estimate 3D Hand Pose from Single RGB Images](https://mlsanity.com/paper/1705.01389/) (2017)
- [Hand Keypoint Detection in Single Images using Multiview Bootstrapping](https://mlsanity.com/paper/1704.07809/) (2017)
- [H+O: Unified Egocentric Recognition of 3D Hand-Object Poses and Interactions](https://mlsanity.com/paper/1904.05349/) (2019)
- [Leveraging Photometric Consistency over Time for Sparsely Supervised Hand-Object Reconstruction](https://mlsanity.com/paper/2004.13449/) (2020)
- [CPF: Learning a Contact Potential Field to Model the Hand-Object Interaction](https://mlsanity.com/paper/2012.00924/) (2020)
- [Keypoint Transformer: Solving Joint Identification in Challenging Hands and Object Interactions for Accurate 3D Pose Estimation](https://mlsanity.com/paper/2104.14639/) (2021)
- [Real-time Hand Tracking under Occlusion from an Egocentric RGB-D Sensor](https://mlsanity.com/paper/1704.02201/) (2017)
- [Real-time Pose and Shape Reconstruction of Two Interacting Hands With a Single Depth Camera](https://mlsanity.com/paper/2106.08059/) (2021)
- [Real-time Joint Tracking of a Hand Manipulating an Object from RGB-D Input](https://mlsanity.com/paper/1610.04889/) (2016)
- [Hierarchical Temporal Transformer for 3D Hand Pose Estimation and Action Recognition from Egocentric RGB Videos](https://mlsanity.com/paper/2209.09484/) (2022)
- [Contrastive Representation Learning for Hand Shape Estimation](https://mlsanity.com/paper/2106.04324/) (2021)
- [State of the Art in Dense Monocular Non-Rigid 3D Reconstruction](https://mlsanity.com/paper/2210.15664/) (2022)
- [THOR-Net: End-to-end Graformer-based Realistic Two Hands and Object Reconstruction with Self-supervision](https://mlsanity.com/paper/2210.13853/) (2022)
- [Transformer-based Global 3D Hand Pose Estimation in Two Hands Manipulating Objects Scenarios](https://mlsanity.com/paper/2210.11384/) (2022)

## Abstract (arXiv)

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. We evaluate ShaRPy on a keypoint detection benchmark and show qualitative results of hand function assessments for activity monitoring of musculoskeletal diseases.