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---
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.
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