README.md

---
license: gpl-3.0
task_categories:
- tabular-classification
- tabular-regression
- image-to-3d
- depth-estimation
pretty_name: AD Trajectories
size_categories:
- 100K<n<1M
---

**Paper in the making**

---

# AD-Trajectories Dataset
This dataset was created for the Master's thesis "From Broadcast to 3D: A Deep Learning Approach for Tennis Trajectory and Spin Estimation" by Alexandra Göppert at the University Augsburg, Chair of Machine Learning and Computer Vision.
The AD-Rallies dataset is a large-scale synthetic dataset generated using the MuJoCo physics engine. It was built to bridge the synthetic-to-real gap by providing highly accurate physical models of aerodynamic forces, such as the Magnus effect, and complex ball-court interactions.

---
## Dataset Overview

The dataset comprises of approxemately 3.2 Million synthetic tennis rallies. The rallies start with a ball toss, a serve and then up to 4 further basic strokes like (groundstroke, volley, lob, short and smash) can be added. All physical kinematics, including the 3D positions, linear velocities, and angular velocities (spin), are captured at a high resolution of 500 frames per second (fps), corresponding to a time step of 0.002 seconds.

The dataset is saved as a .tar file becuase it compromises out of 3.2 Million .npz files. The tar file has a size of 87 GB. 
The npz file includes the position, velocity and angular velocity of the ball through the whole rally. 

The name if the npz files is like follows:
toss_xxxxx_branch_yyy.npz or toss_xxxxx_branch_yyy_deadend.npz

Where xxxxx is the number of one of the 20000 tosses that were initially simulated and start the rally. Combined with a server this creates the stem for the rallies. For each stem rally upto 4 returns are added. These are numbered in increasing order as branch_yyy. The number of max rallies emerging out of one toss-serve combination is 160. 
If  there is no feasable return found the rally is no longer stritched together (even though it has less than a total of 6 shots in the rally). This rally is then marked with "_deadend".

---
# Data Structures per Trajectory
Inside each npz, you will find exactly seven .npy files. These numpy arrays store the spatial, temporal, and camera data for that specific sequence:

    positions.npy: The 3D position of the ball (x, y, z) throughout the rally, recorded at a resolution of 0.002s.
    velocities.npy: The linear velocity of the ball relative to the world coordinate system, recorded at a resolution of 0.002s.
    rotations.npy: The angular velocity (spin) of the ball in all 3 directions, recorded at a resolution of 0.002s.

The position and velocity is defined in relation to the 3D world coordinate system, which is defined like follows:
<img src="./3d_coordinate_system_in_field.png" alt="Coordinate system definition of 3D world coordinates" style="width:50%; height:auto;" >

The ball spin (rotations.npy) is defined in relation to the ball's local coordinate system. The direction of  which is defined as follows:
<img src="./Screenshot 2026-04-20 194926.png" alt="Definition of the ball's local coordinate system" >

---

# Download the Dataset

You can download the specific tar file using the hf_hub_download function. This is more efficient than cloning the entire repository if you only need the archive.
Python

```
from huggingface_hub import hf_hub_download
```

```
REPO_ID = "XSpaceCoderX/AD-Rallies"
FILENAME = "data.tar"

print(f"Downloading {FILENAME}...")

local_path = hf_hub_download(
    repo_id=REPO_ID,
    filename=FILENAME,
    repo_type="dataset"
)

print(f"File downloaded to: {local_path}")
```

## Unpack the Dataset

Once downloaded, you can extract the contents using Python's built-in tarfile module or a system command.
Option A: Using Python (Cross-platform)

This is the recommended way to ensure compatibility across Windows, macOS, and Linux.

```
import tarfile
import os

def extract_tar(file_path, extract_path="."):
    print(f"Extracting {file_path}...")
    with tarfile.open(file_path, "r") as tar:
        tar.extractall(path=extract_path)
    print("Extraction complete!")
```

Note: Make sure you have at least 180GB of free disk space (87 GB for the archive + 100 GB for the extracted contents).