# WorldPose: A World Cup Dataset for Global 3D Human Pose Estimation

arXiv: https://arxiv.org/abs/2501.02771 · HTML: https://mlsanity.com/paper/2501.02771/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 52 of its 81 references that are in the map.
- First posted on arXiv 6 Jan 2025.

## Builds on (most cited first, 30 of 52)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [End-to-end Recovery of Human Shape and Pose](https://mlsanity.com/paper/1712.06584/) (2017)
- [ByteTrack: Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2110.06864/) (2021)
- [Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image](https://mlsanity.com/paper/1607.08128/) (2016)
- [DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras](https://mlsanity.com/paper/2108.10869/) (2021)
- [Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the Loop](https://mlsanity.com/paper/1909.12828/) (2019)
- [VIBE: Video Inference for Human Body Pose and Shape Estimation](https://mlsanity.com/paper/1912.05656/) (2019)
- [Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision](https://mlsanity.com/paper/1611.09813/) (2016)
- [ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation](https://mlsanity.com/paper/2204.12484/) (2022)
- [Humans in 4D: Reconstructing and Tracking Humans with Transformers](https://mlsanity.com/paper/2305.20091/) (2023)
- [PARE: Part Attention Regressor for 3D Human Body Estimation](https://mlsanity.com/paper/2104.08527/) (2021)
- [HuMoR: 3D Human Motion Model for Robust Pose Estimation](https://mlsanity.com/paper/2105.04668/) (2021)
- [HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimation](https://mlsanity.com/paper/2011.14672/) (2020)
- [Resolving 3D Human Pose Ambiguities with 3D Scene Constraints](https://mlsanity.com/paper/1908.06963/) (2019)
- [PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop](https://mlsanity.com/paper/2103.16507/) (2021)
- [CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimation](https://mlsanity.com/paper/2208.00571/) (2022)
- [BEDLAM: A Synthetic Dataset of Bodies Exhibiting Detailed Lifelike Animated Motion](https://mlsanity.com/paper/2306.16940/) (2023)
- [Monocular, One-stage, Regression of Multiple 3D People](https://mlsanity.com/paper/2008.12272/) (2020)
- [Unite the People: Closing the Loop Between 3D and 2D Human Representations](https://mlsanity.com/paper/1701.02468/) (2017)
- [Single-Shot Multi-Person 3D Pose Estimation From Monocular RGB](https://mlsanity.com/paper/1712.03453/) (2017)
- [Capturing and Inferring Dense Full-Body Human-Scene Contact](https://mlsanity.com/paper/2206.09553/) (2022)
- [GLAMR: Global Occlusion-Aware Human Mesh Recovery with Dynamic Cameras](https://mlsanity.com/paper/2112.01524/) (2021)
- [SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos](https://mlsanity.com/paper/1804.04527/) (2018)
- [Putting People in their Place: Monocular Regression of 3D People in Depth](https://mlsanity.com/paper/2112.08274/) (2021)
- [Decoupling Human and Camera Motion from Videos in the Wild](https://mlsanity.com/paper/2302.12827/) (2023)
- [EgoBody: Human Body Shape and Motion of Interacting People from Head-Mounted Devices](https://mlsanity.com/paper/2112.07642/) (2021)
- [SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos](https://mlsanity.com/paper/2011.13367/) (2020)
- [EMDB: The Electromagnetic Database of Global 3D Human Pose and Shape in the Wild](https://mlsanity.com/paper/2308.16894/) (2023)
- [TRACE: 5D Temporal Regression of Avatars with Dynamic Cameras in 3D Environments](https://mlsanity.com/paper/2306.02850/) (2023)

## Built on by (most cited first, 14 of 14)

- [SoccerNet-v3D: Leveraging Sports Broadcast Replays for 3D Scene Understanding](https://mlsanity.com/paper/2504.10106/) (2025)
- [PHD: Personalized 3D Human Body Fitting with Point Diffusion](https://mlsanity.com/paper/2508.21257/) (2025)
- [Multi-person Physics-based Pose Estimation for Combat Sports](https://mlsanity.com/paper/2504.08175/) (2025)
- [HHI-Assist: A Dataset and Benchmark of Human-Human Interaction in Physical Assistance Scenario](https://mlsanity.com/paper/2509.10096/) (2025)
- [CalTennis: Large Multi-View Tennis Video Dataset and Benchmark of Monocular-to-3D Pose Estimation](https://mlsanity.com/paper/2606.20542/) (2026)
- [SoccerNet 2026 Challenges Results](https://mlsanity.com/paper/2607.07320/) (2026)
- [SoccerTrack v2: A Full-Pitch Panoramic Video Dataset for Game State Reconstruction and Ball Action Spotting](https://mlsanity.com/paper/2508.01802/) (2025)
- [SimpliHuMoN: Simplifying Human Motion Prediction](https://mlsanity.com/paper/2603.04399/) (2026)
- [Sparkle: A Robust and Versatile Representation for Point Cloud based Human Motion Capture](https://mlsanity.com/paper/2604.00857/) (2026)
- [Mesh-Aware Epipolar Matching for Multi-View Multi-Person 3D Pose Estimation in Basketball](https://mlsanity.com/paper/2605.29953/) (2026)
- [SMART: SMPLest-X Mesh Adaptation and RAFT Tracking for Soccer Pose Estimation](https://mlsanity.com/paper/2605.31551/) (2026)
- [Capturing Uncertainty in Human Motion for Representation Learning in Soccer](https://mlsanity.com/paper/2608.11203/) (2026)
- [Field Converter: Geometry-Initialized Temporal Residual Refinement for World-Grounded Player Pose Estimation from Soccer Broadcasts](https://mlsanity.com/paper/2609.10498/) (2026)
- [A Diagnostic Study of World-Space Human Recovery Under High Camera Dynamics](https://mlsanity.com/paper/2610.19729/) (2026)

## Abstract (arXiv)

We present WorldPose, a novel dataset for advancing research in multi-person global pose estimation in the wild, featuring footage from the 2022 FIFA World Cup. While previous datasets have primarily focused on local poses, often limited to a single person or in constrained, indoor settings, the infrastructure deployed for this sporting event allows access to multiple fixed and moving cameras in different stadiums. We exploit the static multi-view setup of HD cameras to recover the 3D player poses and motions with unprecedented accuracy given capture areas of more than 1.75 acres. We then leverage the captured players' motions and field markings to calibrate a moving broadcasting camera. The resulting dataset comprises more than 80 sequences with approx 2.5 million 3D poses and a total traveling distance of over 120 km. Subsequently, we conduct an in-depth analysis of the SOTA methods for global pose estimation. Our experiments demonstrate that WorldPose challenges existing multi-person techniques, supporting the potential for new research in this area and others, such as sports analysis. All pose annotations (in SMPL format), broadcasting camera parameters and footage will be released for academic research purposes.