# A review of 3D human pose estimation algorithms for markerless motion capture

arXiv: https://arxiv.org/abs/2010.06449 · HTML: https://mlsanity.com/paper/2010.06449/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2020.

## Built on by (most cited first, 24 of 24)

- [Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks](https://mlsanity.com/paper/2302.08890/) (2023)
- [IMUPoser: Full-Body Pose Estimation using IMUs in Phones, Watches, and Earbuds](https://mlsanity.com/paper/2304.12518/) (2023)
- [DeciWatch: A Simple Baseline for 10x Efficient 2D and 3D Pose Estimation](https://mlsanity.com/paper/2203.08713/) (2022)
- [DisCoRD: Discrete Tokens to Continuous Motion via Rectified Flow Decoding](https://mlsanity.com/paper/2411.19527/) (2024)
- [Dual networks based 3D Multi-Person Pose Estimation from Monocular Video](https://mlsanity.com/paper/2205.00748/) (2022)
- [Markerless human pose estimation for biomedical applications: a survey](https://mlsanity.com/paper/2308.00519/) (2023)
- [Markerless Multi-view 3D Human Pose Estimation: a survey](https://mlsanity.com/paper/2407.03817/) (2024)
- [OpenCap Monocular: 3D Human Kinematics and Musculoskeletal Dynamics from a Single Smartphone Video](https://mlsanity.com/paper/2603.24733/) (2026)
- [Domain-Adaptive 2D Human Pose Estimation via Dual Teachers in Extremely Low-Light Conditions](https://mlsanity.com/paper/2407.15451/) (2024)
- [Fusing uncalibrated IMUs and handheld smartphone video to reconstruct knee kinematics](https://mlsanity.com/paper/2405.17368/) (2024)
- [Bottom-Up 2D Pose Estimation via Dual Anatomical Centers for Small-Scale Persons](https://mlsanity.com/paper/2208.11975/) (2022)
- [WheelPoser: Sparse-IMU Based Body Pose Estimation for Wheelchair Users](https://mlsanity.com/paper/2409.08494/) (2024)
- [Automatic Assessment of Functional Movement Screening Exercises with Deep Learning Architectures](https://mlsanity.com/paper/2210.01209/) (2022)
- [Fine-grained Human Activity Recognition Using Virtual On-body Acceleration Data](https://mlsanity.com/paper/2211.01342/) (2022)
- [Markerless 3D human pose tracking through multiple cameras and AI: Enabling high accuracy, robustness, and real-time performance](https://mlsanity.com/paper/2303.18119/) (2023)
- [Strengths and Weaknesses of 3D Pose Estimation and Inertial Motion Capture System for Movement Therapy](https://mlsanity.com/paper/2306.06117/) (2023)
- [EVOPOSE: A Recursive Transformer For 3D Human Pose Estimation With Kinematic Structure Priors](https://mlsanity.com/paper/2306.09615/) (2023)
- [Validation of Human Pose Estimation and Human Mesh Recovery for Extracting Clinically Relevant Motion Data from Videos](https://mlsanity.com/paper/2503.14760/) (2025)
- [Grounding Intelligence in Movement](https://mlsanity.com/paper/2507.02771/) (2025)
- [DancingBox: A Lightweight MoCap System for Character Animation from Physical Proxies](https://mlsanity.com/paper/2603.17704/) (2026)
- [Topology-Preserved Human Reconstruction with Details](https://mlsanity.com/paper/2106.06313/) (2021)
- [Predictive Modeling of Equine Activity Budgets Using a 3D Skeleton Reconstructed from Surveillance Recordings](https://mlsanity.com/paper/2306.05311/) (2023)
- [HDiffTG: A Lightweight Hybrid Diffusion-Transformer-GCN Architecture for 3D Human Pose Estimation](https://mlsanity.com/paper/2505.04276/) (2025)
- [COMETH: Convex Optimization for Multiview Estimation and Tracking of Humans](https://mlsanity.com/paper/2508.20920/) (2025)

## Abstract (arXiv)

Human pose estimation is a very active research field, stimulated by its important applications in robotics, entertainment or health and sports sciences, among others. Advances in convolutional networks triggered noticeable improvements in 2D pose estimation, leading modern 3D markerless motion capture techniques to an average error per joint of 20 mm. However, with the proliferation of methods, it is becoming increasingly difficult to make an informed choice. Here, we review the leading human pose estimation methods of the past five years, focusing on metrics, benchmarks and method structures. We propose a taxonomy based on accuracy, speed and robustness that we use to classify de methods and derive directions for future research.