# Monocular 3D Human Pose Estimation for Sports Broadcasts using Partial Sports Field Registration

arXiv: https://arxiv.org/abs/2304.04437 · HTML: https://mlsanity.com/paper/2304.04437/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 32 references that are in the map.
- First posted on arXiv 10 Apr 2023.

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

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [MediaPipe: A Framework for Building Perception Pipelines](https://mlsanity.com/paper/1906.08172/) (2019)
- [ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation](https://mlsanity.com/paper/2204.12484/) (2022)
- [Integral Human Pose Regression](https://mlsanity.com/paper/1711.08229/) (2017)
- [Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Pose](https://mlsanity.com/paper/1611.07828/) (2016)
- [Whole-Body Human Pose Estimation in the Wild](https://mlsanity.com/paper/2007.11858/) (2020)
- [Exploiting Temporal Contexts with Strided Transformer for 3D Human Pose Estimation](https://mlsanity.com/paper/2103.14304/) (2021)
- [PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimation](https://mlsanity.com/paper/2105.02465/) (2021)
- [Conditional Directed Graph Convolution for 3D Human Pose Estimation](https://mlsanity.com/paper/2107.07797/) (2021)
- [Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats](https://mlsanity.com/paper/2212.14474/) (2022)
- [MeTRAbs: Metric-Scale Truncation-Robust Heatmaps for Absolute 3D Human Pose Estimation](https://mlsanity.com/paper/2007.07227/) (2020)
- [Sports Camera Calibration via Synthetic Data](https://mlsanity.com/paper/1810.10658/) (2018)
- [TVCalib: Camera Calibration for Sports Field Registration in Soccer](https://mlsanity.com/paper/2207.11709/) (2022)
- [Improving Robustness and Accuracy via Relative Information Encoding in 3D Human Pose Estimation](https://mlsanity.com/paper/2107.13994/) (2021)
- [Consensus-based Optimization for 3D Human Pose Estimation in Camera Coordinates](https://mlsanity.com/paper/1911.09245/) (2019)

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

- [ExpertAF: Expert Actionable Feedback from Video](https://mlsanity.com/paper/2408.00672/) (2024)
- [A Universal Protocol to Benchmark Camera Calibration for Sports](https://mlsanity.com/paper/2404.09807/) (2024)
- [Active Transfer Learning for Efficient Video-Specific Human Pose Estimation](https://mlsanity.com/paper/2311.05041/) (2023)
- [Towards Ball Spin and Trajectory Analysis in Table Tennis Broadcast Videos via Physically Grounded Synthetic-to-Real Transfer](https://mlsanity.com/paper/2504.19863/) (2025)
- [Benchmarking 3D Human Pose Estimation Models under Occlusions](https://mlsanity.com/paper/2504.10350/) (2025)
- [AJAHR: Amputated Joint Aware 3D Human Mesh Recovery](https://mlsanity.com/paper/2509.19939/) (2025)
- [HumanScore: Benchmarking Human Motions in Generated Videos](https://mlsanity.com/paper/2604.20157/) (2026)
- [Leveraging Anthropometric Measurements to Improve Human Mesh Estimation and Ensure Consistent Body Shapes](https://mlsanity.com/paper/2409.17671/) (2024)
- [Age-Inclusive 3D Human Mesh Recovery for Action-Preserving Data Anonymization](https://mlsanity.com/paper/2512.05259/) (2025)
- [HiReFF: High-Resolution Feedforward Human Reconstruction from Uncalibrated Sparse-View Video](https://mlsanity.com/paper/2606.29333/) (2026)
- [Joint angle based learning to refine kinematic human pose estimation](https://mlsanity.com/paper/2507.11075/) (2025)
- [Multi-Person Pose Estimation Evaluation Using Optimal Transportation and Improved Pose Matching](https://mlsanity.com/paper/2603.10398/) (2026)
- [Field Converter: Geometry-Initialized Temporal Residual Refinement for World-Grounded Player Pose Estimation from Soccer Broadcasts](https://mlsanity.com/paper/2609.10498/) (2026)

## Abstract (arXiv)

The filming of sporting events projects and flattens the movement of athletes in the world onto a 2D broadcast image. The pixel locations of joints in these images can be detected with high validity. Recovering the actual 3D movement of the limbs (kinematics) of the athletes requires lifting these 2D pixel locations back into a third dimension, implying a certain scene geometry. The well-known line markings of sports fields allow for the calibration of the camera and for determining the actual geometry of the scene. Close-up shots of athletes are required to extract detailed kinematics, which in turn obfuscates the pertinent field markers for camera calibration. We suggest partial sports field registration, which determines a set of scene-consistent camera calibrations up to a single degree of freedom. Through joint optimization of 3D pose estimation and camera calibration, we demonstrate the successful extraction of 3D running kinematics on a 400m track. In this work, we combine advances in 2D human pose estimation and camera calibration via partial sports field registration to demonstrate an avenue for collecting valid large-scale kinematic datasets. We generate a synthetic dataset of more than 10k images in Unreal Engine 5 with different viewpoints, running styles, and body types, to show the limitations of existing monocular 3D HPE methods. Synthetic data and code are available at https://github.com/tobibaum/PartialSportsFieldReg_3DHPE.