# VideoRun2D Demo: Markerless Body Tracking for Biomechanical Analysis of Running

arXiv: https://arxiv.org/abs/2608.19480 · HTML: https://mlsanity.com/paper/2608.19480/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 47 references that are in the map.
- First posted on arXiv 19 Aug 2026.

## Builds on (most cited first, 17 of 17)

- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation](https://mlsanity.com/paper/2204.12484/) (2022)
- [RTMPose: Real-Time Multi-Person Pose Estimation based on MMPose](https://mlsanity.com/paper/2303.07399/) (2023)
- [Facial Soft Biometrics for Recognition in the Wild: Recent Works, Annotation, and COTS Evaluation](https://mlsanity.com/paper/2210.13129/) (2022)
- [Smartphone Sensors for Modeling Human-Computer Interaction: General Outlook and Research Datasets for User Authentication](https://mlsanity.com/paper/2006.00790/) (2020)
- [A multimodal dataset for understanding the impact of mobile phones on remote online virtual education](https://mlsanity.com/paper/2412.14195/) (2024)
- [Is My Vision-Language Data in Your AI? Membership Inference Test (MINT) Demo 2](https://mlsanity.com/paper/2606.14748/) (2026)
- [Personalized Weight Loss Management through Wearable Devices and Artificial Intelligence](https://mlsanity.com/paper/2409.08700/) (2024)
- [Exploiting Multiple Representations: 3D Face Biometrics Fusion with Application to Surveillance](https://mlsanity.com/paper/2504.18886/) (2025)
- [AttZoom: Attention Zoom for Better Visual Features](https://mlsanity.com/paper/2508.03625/) (2025)
- [Overview of Risk Assessment and Management for Intelligent Systems under the AI Act and Beyond](https://mlsanity.com/paper/2607.02197/) (2026)
- [Exploring 3D Face Reconstruction and Fusion Methods for Face Verification: A Case-Study in Video Surveillance](https://mlsanity.com/paper/2409.10481/) (2024)
- [Unraveling Machine Behavior by Multi-Level Bias Analysis and Detection: Methodology and Application to Computer Vision](https://mlsanity.com/paper/2607.07236/) (2026)
- [VideoRun2D: Cost-Effective Markerless Motion Capture for Sprint Biomechanics](https://mlsanity.com/paper/2409.10175/) (2024)
- [Comparison of Visual Trackers for Biomechanical Analysis of Running](https://mlsanity.com/paper/2505.04713/) (2025)
- [AG-VPReID 2025: Aerial-Ground Video-based Person Re-identification Challenge Results](https://mlsanity.com/paper/2506.22843/) (2025)
- [Is Visual Realism Enough? Evaluating Gait Biometric Fidelity in Generative AI Human Animation](https://mlsanity.com/paper/2512.19275/) (2025)

## Abstract (arXiv)

Human pose estimation has advanced significantly due to the development of deep learning models, increased data availability, and improved computing resources. These developments have led to highly accurate body tracking systems with direct applications in sports analysis and performance evaluation. The VideoRun2D Demo performs a biomechanical analysis during sprints using different human pose estimators. The proposed framework was evaluated using human pose trackers and expert manual annotations. The tested framework uses 314 sprints from 44 professional runners, focusing on two key joint angles in sprint biomechanics: 1) hip flexion/extension and 2) knee flexion/extension. The framework also includes a post-processing module for outlier detection. The tested results demonstrate that the average root-mean-square errors range from 11.46deg to 5.83deg for the best trackers. When integrated with the post-processing modules, these errors can be reduced to 9.87deg and 5.30deg, respectively. The VideoRun2D Demo findings suggest that human pose-tracking approaches can be valuable resources for the biomechanical analysis of running.