# SportsPose -- A Dynamic 3D sports pose dataset

arXiv: https://arxiv.org/abs/2304.01865 · HTML: https://mlsanity.com/paper/2304.01865/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 49 references that are in the map.
- First posted on arXiv 4 Apr 2023.

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

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [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)
- [Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision](https://mlsanity.com/paper/1611.09813/) (2016)
- [End-to-End Human Pose and Mesh Reconstruction with Transformers](https://mlsanity.com/paper/2012.09760/) (2020)
- [3D human pose estimation in video with temporal convolutions and semi-supervised training](https://mlsanity.com/paper/1811.11742/) (2018)
- [PARE: Part Attention Regressor for 3D Human Body Estimation](https://mlsanity.com/paper/2104.08527/) (2021)
- [Mesh Graphormer](https://mlsanity.com/paper/2104.00272/) (2021)
- [Monocular, One-stage, Regression of Multiple 3D People](https://mlsanity.com/paper/2008.12272/) (2020)
- [MixSTE: Seq2seq Mixed Spatio-Temporal Encoder for 3D Human Pose Estimation in Video](https://mlsanity.com/paper/2203.00859/) (2022)
- [P-STMO: Pre-Trained Spatial Temporal Many-to-One Model for 3D Human Pose Estimation](https://mlsanity.com/paper/2203.07628/) (2022)
- [Benchmarking and Error Diagnosis in Multi-Instance Pose Estimation](https://mlsanity.com/paper/1707.05388/) (2017)
- [Out-of-Domain Human Mesh Reconstruction via Dynamic Bilevel Online Adaptation](https://mlsanity.com/paper/2111.04017/) (2021)
- [Soccer on Your Tabletop](https://mlsanity.com/paper/1806.00890/) (2018)
- [Anatomy-aware 3D Human Pose Estimation with Bone-based Pose Decomposition](https://mlsanity.com/paper/2002.10322/) (2020)

## Built on by (most cited first, 26 of 26)

- [FinePOSE: Fine-Grained Prompt-Driven 3D Human Pose Estimation via Diffusion Models](https://mlsanity.com/paper/2405.05216/) (2024)
- [AutoSoccerPose: Automated 3D posture Analysis of Soccer Shot Movements](https://mlsanity.com/paper/2405.12070/) (2024)
- [AthletePose3D: A Benchmark Dataset for 3D Human Pose Estimation and Kinematic Validation in Athletic Movements](https://mlsanity.com/paper/2503.07499/) (2025)
- [A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision](https://mlsanity.com/paper/2307.03353/) (2023)
- [GST: Precise 3D Human Body from a Single Image with Gaussian Splatting Transformers](https://mlsanity.com/paper/2409.04196/) (2024)
- [3D Pose-Based Temporal Action Segmentation for Figure Skating: A Fine-Grained and Jump Procedure-Aware Annotation Approach](https://mlsanity.com/paper/2408.16638/) (2024)
- [Optimal-state Dynamics Estimation for Physics-based Human Motion Capture from Videos](https://mlsanity.com/paper/2410.07795/) (2024)
- [AthleticsPose: Authentic Sports Motion Dataset on Athletic Field and Evaluation of Monocular 3D Pose Estimation Ability](https://mlsanity.com/paper/2507.12905/) (2025)
- [Gen4D: Synthesizing Humans and Scenes in the Wild](https://mlsanity.com/paper/2506.05397/) (2025)
- [QuaMo: Quaternion Motions for Vision-based 3D Human Kinematics Capture](https://mlsanity.com/paper/2601.19580/) (2026)
- [Leveraging Anthropometric Measurements to Improve Human Mesh Estimation and Ensure Consistent Body Shapes](https://mlsanity.com/paper/2409.17671/) (2024)
- [Multi-hop graph transformer network for 3D human pose estimation](https://mlsanity.com/paper/2405.03055/) (2024)
- [Investigating Event-Based Cameras for Video Frame Interpolation in Sports](https://mlsanity.com/paper/2407.02370/) (2024)
- [PoseAdapt: Sustainable Human Pose Estimation via Continual Learning Benchmarks and Toolkit](https://mlsanity.com/paper/2409.20469/) (2024)
- [Multi-person Physics-based Pose Estimation for Combat Sports](https://mlsanity.com/paper/2504.08175/) (2025)
- [Systematic Comparison of Projection Methods for Monocular 3D Human Pose Estimation on Fisheye Images](https://mlsanity.com/paper/2506.19747/) (2025)
- [RefereeBench: Are Video MLLMs Ready to be Multi-Sport Referees](https://mlsanity.com/paper/2604.15736/) (2026)
- [CalTennis: Large Multi-View Tennis Video Dataset and Benchmark of Monocular-to-3D Pose Estimation](https://mlsanity.com/paper/2606.20542/) (2026)
- [Two Views Are Better than One: Monocular 3D Pose Estimation with Multiview Consistency](https://mlsanity.com/paper/2311.12421/) (2023)
- [BundleMoCap: Efficient, Robust and Smooth Motion Capture from Sparse Multiview Videos](https://mlsanity.com/paper/2311.12679/) (2023)
- [Women Sport Actions Dataset for Visual Classification Using Small Scale Training Data](https://mlsanity.com/paper/2507.10969/) (2025)
- [KASportsFormer: Kinematic Anatomy Enhanced Transformer for 3D Human Pose Estimation on Short Sports Scene Video](https://mlsanity.com/paper/2507.20763/) (2025)
- [VIFSS: View-Invariant and Figure Skating-Specific Pose Representation Learning for Temporal Action Segmentation](https://mlsanity.com/paper/2508.10281/) (2025)
- [Mesh-Aware Epipolar Matching for Multi-View Multi-Person 3D Pose Estimation in Basketball](https://mlsanity.com/paper/2605.29953/) (2026)
- [Capturing Uncertainty in Human Motion for Representation Learning in Soccer](https://mlsanity.com/paper/2608.11203/) (2026)
- [HSTGFormer: Hyper Spatial-Temporal Graph Transformer for 3D Human Pose Estimation](https://mlsanity.com/paper/2608.12187/) (2026)

## Abstract (arXiv)

Accurate 3D human pose estimation is essential for sports analytics, coaching, and injury prevention. However, existing datasets for monocular pose estimation do not adequately capture the challenging and dynamic nature of sports movements. In response, we introduce SportsPose, a large-scale 3D human pose dataset consisting of highly dynamic sports movements. With more than 176,000 3D poses from 24 different subjects performing 5 different sports activities, SportsPose provides a diverse and comprehensive set of 3D poses that reflect the complex and dynamic nature of sports movements. Contrary to other markerless datasets we have quantitatively evaluated the precision of SportsPose by comparing our poses with a commercial marker-based system and achieve a mean error of 34.5 mm across all evaluation sequences. This is comparable to the error reported on the commonly used 3DPW dataset. We further introduce a new metric, local movement, which describes the movement of the wrist and ankle joints in relation to the body. With this, we show that SportsPose contains more movement than the Human3.6M and 3DPW datasets in these extremum joints, indicating that our movements are more dynamic. The dataset with accompanying code can be downloaded from our website. We hope that SportsPose will allow researchers and practitioners to develop and evaluate more effective models for the analysis of sports performance and injury prevention. With its realistic and diverse dataset, SportsPose provides a valuable resource for advancing the state-of-the-art in pose estimation in sports.