# Soccer on Your Tabletop

arXiv: https://arxiv.org/abs/1806.00890 · HTML: https://mlsanity.com/paper/1806.00890/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2018.

## Built on by (most cited first, 20 of 20)

- [GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection](https://mlsanity.com/paper/2103.17202/) (2021)
- [Consistent Depth of Moving Objects in Video](https://mlsanity.com/paper/2108.01166/) (2021)
- [DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection](https://mlsanity.com/paper/2207.10758/) (2022)
- [SportsCap: Monocular 3D Human Motion Capture and Fine-grained Understanding in Challenging Sports Videos](https://mlsanity.com/paper/2104.11452/) (2021)
- [SAIL-VOS 3D: A Synthetic Dataset and Baselines for Object Detection and 3D Mesh Reconstruction from Video Data](https://mlsanity.com/paper/2105.08612/) (2021)
- [Temporally-Aware Feature Pooling for Action Spotting in Soccer Broadcasts](https://mlsanity.com/paper/2104.06779/) (2021)
- [The One Where They Reconstructed 3D Humans and Environments in TV Shows](https://mlsanity.com/paper/2207.14279/) (2022)
- [SportsPose -- A Dynamic 3D sports pose dataset](https://mlsanity.com/paper/2304.01865/) (2023)
- [Out-of-Domain Human Mesh Reconstruction via Dynamic Bilevel Online Adaptation](https://mlsanity.com/paper/2111.04017/) (2021)
- [FinePOSE: Fine-Grained Prompt-Driven 3D Human Pose Estimation via Diffusion Models](https://mlsanity.com/paper/2405.05216/) (2024)
- [WorldPose: A World Cup Dataset for Global 3D Human Pose Estimation](https://mlsanity.com/paper/2501.02771/) (2025)
- [UltraPose: Synthesizing Dense Pose with 1 Billion Points by Human-body Decoupling 3D Model](https://mlsanity.com/paper/2110.15267/) (2021)
- [Self-supervised 3D Human Pose Estimation from a Single Image](https://mlsanity.com/paper/2304.02349/) (2023)
- [3D Human Pose Estimation Using M"obius Graph Convolutional Networks](https://mlsanity.com/paper/2203.10554/) (2022)
- [SuperCaustics: Real-time, open-source simulation of transparent objects for deep learning applications](https://mlsanity.com/paper/2107.11008/) (2021)
- [Multi-view data capture for dynamic object reconstruction using handheld augmented reality mobiles](https://mlsanity.com/paper/2103.07883/) (2021)
- [A Survey of Advanced Computer Vision Techniques for Sports](https://mlsanity.com/paper/2301.07583/) (2023)
- [Where Is The Ball: 3D Ball Trajectory Estimation From 2D Monocular Tracking](https://mlsanity.com/paper/2506.05763/) (2025)
- [DreamPose3D: Hallucinative Diffusion with Prompt Learning for 3D Human Pose Estimation](https://mlsanity.com/paper/2511.09502/) (2025)
- [TCG-AR: Real-Time Multi-View Augmented Reality for Trading Card Game Streaming](https://mlsanity.com/paper/2607.02090/) (2026)

## Abstract (arXiv)

We present a system that transforms a monocular video of a soccer game into a moving 3D reconstruction, in which the players and field can be rendered interactively with a 3D viewer or through an Augmented Reality device. At the heart of our paper is an approach to estimate the depth map of each player, using a CNN that is trained on 3D player data extracted from soccer video games. We compare with state of the art body pose and depth estimation techniques, and show results on both synthetic ground truth benchmarks, and real YouTube soccer footage.