# Markerless human pose estimation for biomedical applications: a survey

arXiv: https://arxiv.org/abs/2308.00519 · HTML: https://mlsanity.com/paper/2308.00519/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 43 of its 119 references that are in the map.
- First posted on arXiv 1 Aug 2023.

## Builds on (most cited first, 30 of 43)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (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)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [3D human pose estimation in video with temporal convolutions and semi-supervised training](https://mlsanity.com/paper/1811.11742/) (2018)
- [Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2107.12213/) (2021)
- [DeepPose: Human Pose Estimation via Deep Neural Networks](https://mlsanity.com/paper/1312.4659/) (2013)
- [Associative Embedding: End-to-End Learning for Joint Detection and Grouping](https://mlsanity.com/paper/1611.05424/) (2016)
- [Markerless tracking of user-defined features with deep learning](https://mlsanity.com/paper/1804.03142/) (2018)
- [AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time](https://mlsanity.com/paper/2211.03375/) (2022)
- [Whole-Body Human Pose Estimation in the Wild](https://mlsanity.com/paper/2007.11858/) (2020)
- [V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map](https://mlsanity.com/paper/1711.07399/) (2017)
- [Not All Tokens Are Equal: Human-centric Visual Analysis via Token Clustering Transformer](https://mlsanity.com/paper/2204.08680/) (2022)
- [ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network](https://mlsanity.com/paper/1811.11431/) (2018)
- [RepNet: Weakly Supervised Training of an Adversarial Reprojection Network for 3D Human Pose Estimation](https://mlsanity.com/paper/1902.09868/) (2019)
- [Probabilistic Monocular 3D Human Pose Estimation with Normalizing Flows](https://mlsanity.com/paper/2107.13788/) (2021)
- [Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image](https://mlsanity.com/paper/1701.00295/) (2017)
- [CanonPose: Self-Supervised Monocular 3D Human Pose Estimation in the Wild](https://mlsanity.com/paper/2011.14679/) (2020)
- [MVOR: A Multi-view RGB-D Operating Room Dataset for 2D and 3D Human Pose Estimation](https://mlsanity.com/paper/1808.08180/) (2018)
- [UniPose: Unified Human Pose Estimation in Single Images and Videos](https://mlsanity.com/paper/2001.08095/) (2020)
- [Faster VoxelPose: Real-time 3D Human Pose Estimation by Orthographic Projection](https://mlsanity.com/paper/2207.10955/) (2022)
- [Multi-View Multi-Person 3D Pose Estimation with Plane Sweep Stereo](https://mlsanity.com/paper/2104.02273/) (2021)
- [Pose Forecasting in Industrial Human-Robot Collaboration](https://mlsanity.com/paper/2208.07308/) (2022)
- [A review of 3D human pose estimation algorithms for markerless motion capture](https://mlsanity.com/paper/2010.06449/) (2020)
- [Robust Estimation of 3D Human Poses from a Single Image](https://mlsanity.com/paper/1406.2282/) (2014)
- [Learning and Tracking the 3D Body Shape of Freely Moving Infants from RGB-D sequences](https://mlsanity.com/paper/1810.07538/) (2018)
- [A Multi-view RGB-D Approach for Human Pose Estimation in Operating Rooms](https://mlsanity.com/paper/1701.07372/) (2017)
- [Weakly-supervised Learning of Human Dynamics](https://mlsanity.com/paper/2007.08969/) (2020)

## Built on by (most cited first, 13 of 13)

- [GAITGen: Disentangled Motion-Pathology Impaired Gait Generative Model -- Bringing Motion Generation to the Clinical Domain](https://mlsanity.com/paper/2503.22397/) (2025)
- [M4Human: A Large-Scale Multimodal mmWave Radar Benchmark for Human Mesh Reconstruction](https://mlsanity.com/paper/2512.12378/) (2025)
- [Deep Learning Approaches for Seizure Video Analysis: A Review](https://mlsanity.com/paper/2312.10930/) (2023)
- [CARE-PD: A Multi-Site Anonymized Clinical Dataset for Parkinson's Disease Gait Assessment](https://mlsanity.com/paper/2510.04312/) (2025)
- [Continuous Patient Monitoring with AI: Real-Time Analysis of Video in Hospital Care Settings](https://mlsanity.com/paper/2412.13152/) (2024)
- [Benchmarking 3D Human Pose Estimation Models under Occlusions](https://mlsanity.com/paper/2504.10350/) (2025)
- [Breaking Coordinate Overfitting: Geometry-Aware WiFi Sensing for Cross-Layout 3D Pose Estimation](https://mlsanity.com/paper/2601.12252/) (2026)
- [PoseLLM: Enhancing Language-Guided Human Pose Estimation with MLP Alignment](https://mlsanity.com/paper/2507.09139/) (2025)
- [Paving the Way Towards Kinematic Assessment Using Monocular Video: A Preclinical Benchmark of State-of-the-Art Deep-Learning-Based 3D Human Pose Estimators Against Inertial Sensors in Daily Living Activities](https://mlsanity.com/paper/2510.02264/) (2025)
- [PRGCN: A Graph Memory Network for Cross-Sequence Pattern Reuse in 3D Human Pose Estimation](https://mlsanity.com/paper/2510.19475/) (2025)
- [Physics Informed Human Posture Estimation Based on 3D Landmarks from Monocular RGB-Videos](https://mlsanity.com/paper/2512.06783/) (2025)
- [DisPOSE: Projected Polystochastic Diffusion for Self-Supervised Multi-View 3D Human Pose Estimation](https://mlsanity.com/paper/2606.07419/) (2026)
- [RePos: Relative-to-Absolute Pose Factorization for Cross-Environment WiFi-Based 3D Human Pose Estimation](https://mlsanity.com/paper/2607.02986/) (2026)

## Abstract (arXiv)

Markerless Human Pose Estimation (HPE) proved its potential to support decision making and assessment in many fields of application. HPE is often preferred to traditional marker-based Motion Capture systems due to the ease of setup, portability, and affordable cost of the technology. However, the exploitation of HPE in biomedical applications is still under investigation. This review aims to provide an overview of current biomedical applications of HPE. In this paper, we examine the main features of HPE approaches and discuss whether or not those features are of interest to biomedical applications. We also identify those areas where HPE is already in use and present peculiarities and trends followed by researchers and practitioners. We include here 25 approaches to HPE and more than 40 studies of HPE applied to motor development assessment, neuromuscolar rehabilitation, and gait & posture analysis. We conclude that markerless HPE offers great potential for extending diagnosis and rehabilitation outside hospitals and clinics, toward the paradigm of remote medical care.