# PosePipe: Open-Source Human Pose Estimation Pipeline for Clinical Research arXiv: https://arxiv.org/abs/2203.08792 ยท HTML: https://mlsanity.com/paper/2203.08792/ ## Key facts - Cited by 10 papers in the map (data through 14 Oct 2026). - Builds on 35 of its 70 references that are in the map. - First posted on arXiv 16 Mar 2022. ## Builds on (most cited first, 30 of 35) - [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014) - [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019) - [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019) - [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019) - [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016) - [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017) - [End-to-end Recovery of Human Shape and Pose](https://mlsanity.com/paper/1712.06584/) (2017) - [Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image](https://mlsanity.com/paper/1607.08128/) (2016) - [Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the Loop](https://mlsanity.com/paper/1909.12828/) (2019) - [VIBE: Video Inference for Human Body Pose and Shape Estimation](https://mlsanity.com/paper/1912.05656/) (2019) - [FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking](https://mlsanity.com/paper/2004.01888/) (2020) - [AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions](https://mlsanity.com/paper/1705.08421/) (2017) - [3D human pose estimation in video with temporal convolutions and semi-supervised training](https://mlsanity.com/paper/1811.11742/) (2018) - [A simple yet effective baseline for 3d human pose estimation](https://mlsanity.com/paper/1705.03098/) (2017) - [TransTrack: Multiple Object Tracking with Transformer](https://mlsanity.com/paper/2012.15460/) (2020) - [PARE: Part Attention Regressor for 3D Human Body Estimation](https://mlsanity.com/paper/2104.08527/) (2021) - [HuMoR: 3D Human Motion Model for Robust Pose Estimation](https://mlsanity.com/paper/2105.04668/) (2021) - [Deep Learning-Based Human Pose Estimation: A Survey](https://mlsanity.com/paper/2012.13392/) (2020) - [Markerless tracking of user-defined features with deep learning](https://mlsanity.com/paper/1804.03142/) (2018) - [Whole-Body Human Pose Estimation in the Wild](https://mlsanity.com/paper/2007.11858/) (2020) - [Distribution-Aware Coordinate Representation for Human Pose Estimation](https://mlsanity.com/paper/1910.06278/) (2019) - [Track to Detect and Segment: An Online Multi-Object Tracker](https://mlsanity.com/paper/2103.08808/) (2021) - [Monocular Expressive Body Regression through Body-Driven Attention](https://mlsanity.com/paper/2008.09062/) (2020) - [Probabilistic Modeling for Human Mesh Recovery](https://mlsanity.com/paper/2108.11944/) (2021) - [GLAMR: Global Occlusion-Aware Human Mesh Recovery with Dynamic Cameras](https://mlsanity.com/paper/2112.01524/) (2021) - [The Frontiers of Fairness in Machine Learning](https://mlsanity.com/paper/1810.08810/) (2018) - [Collaborative Regression of Expressive Bodies using Moderation](https://mlsanity.com/paper/2105.05301/) (2021) - [SPEC: Seeing People in the Wild with an Estimated Camera](https://mlsanity.com/paper/2110.00620/) (2021) - [Recovering 3D Human Mesh from Monocular Images: A Survey](https://mlsanity.com/paper/2203.01923/) (2022) - [Neural Monocular 3D Human Motion Capture with Physical Awareness](https://mlsanity.com/paper/2105.01057/) (2021) ## Built on by (most cited first, 10 of 10) - [Improved Trajectory Reconstruction for Markerless Pose Estimation](https://mlsanity.com/paper/2303.02413/) (2023) - [Markerless Motion Capture and Biomechanical Analysis Pipeline](https://mlsanity.com/paper/2303.10654/) (2023) - [Transforming Gait: Video-Based Spatiotemporal Gait Analysis](https://mlsanity.com/paper/2203.09371/) (2022) - [Portable Biomechanics Laboratory: Clinically Accessible Movement Analysis from a Handheld Smartphone](https://mlsanity.com/paper/2507.08268/) (2025) - [Fusing uncalibrated IMUs and handheld smartphone video to reconstruct knee kinematics](https://mlsanity.com/paper/2405.17368/) (2024) - [Self-Supervised Learning of Gait-Based Biomarkers](https://mlsanity.com/paper/2307.16321/) (2023) - [Monocular Biomechanical Tracking of Fingers with Inverse Kinematics to Foundation Models](https://mlsanity.com/paper/2605.09258/) (2026) - [Advancing Monocular Video-Based Gait Analysis Using Motion Imitation with Physics-Based Simulation](https://mlsanity.com/paper/2402.12676/) (2024) - [BiomechAgent: AI-Assisted Biomechanical Analysis Through Code-Generating Agents](https://mlsanity.com/paper/2602.06975/) (2026) - [Biomechanics-aware Multi-view Markerless Motion Capture of Dexterous Hand Movements](https://mlsanity.com/paper/2607.02796/) (2026) ## Abstract (arXiv) There has been significant progress in machine learning algorithms for human pose estimation that may provide immense value in rehabilitation and movement sciences. However, there remain several challenges to routine use of these tools for clinical practice and translational research, including: 1) high technical barrier to entry, 2) rapidly evolving space of algorithms, 3) challenging algorithmic interdependencies, and 4) complex data management requirements between these components. To mitigate these barriers, we developed a human pose estimation pipeline that facilitates running state-of-the-art algorithms on data acquired in clinical context. Our system allows for running different implementations of several classes of algorithms and handles their interdependencies easily. These algorithm classes include subject identification and tracking, 2D keypoint detection, 3D joint location estimation, and estimating the pose of body models. The system uses a database to manage videos, intermediate analyses, and data for computations at each stage. It also provides tools for data visualization, including generating video overlays that also obscure faces to enhance privacy. Our goal in this work is not to train new algorithms, but to advance the use of cutting-edge human pose estimation algorithms for clinical and translation research. We show that this tool facilitates analyzing large numbers of videos of human movement ranging from gait laboratories analyses, to clinic and therapy visits, to people in the community. We also highlight limitations of these algorithms when applied to clinical populations in a rehabilitation setting.