---
pretty_name: HO-Tracker Challenge
language:
- en
license: mit
task_categories:
- other
tags:
- hand-object
- 3d
- python
size_categories:
- n<10K
---
# HO-Tracker Challenge — HANDS Workshop @ ICCV 2025

## Dataset

Sample training data is provided in [`data/train_sample`](data/train_sample).

To browse the dataset locally:

```bash
# Step 1: Install dependencies
pip install open3d==0.18.0
pip install git+https://github.com/lixiny/manotorch.git

# Step 2: Download the MANO model from https://mano.is.tue.nl/downloads/
#         Place the extracted MANO assets under the `data/` directory
#         (e.g., `data/mano_v1_2`).

# Step 3: Launch the viewer
python vis_demo.py
```

> Note (OakInk V2)
> 
> In OakInk V2, MANO parameters are obtained by fitting to SMPL-X meshes. As a result, hand keypoints computed from MANO may differ from the original OakInk V2 keypoints by a few millimeters. Please choose the set that best suits your use case.