---
dataset_info:
- config_name: full
  features:
  - name: image
    dtype: image
  - name: actuated_angle
    struct:
    - name: '0'
      dtype: int32
    - name: '1'
      dtype: int32
  splits:
  - name: train
    num_bytes: 5561715.0
    num_examples: 5
  download_size: 5564574
  dataset_size: 5561715.0
- config_name: small
  features:
  - name: image
    dtype: image
  - name: actuated_angle
    struct:
    - name: '0'
      dtype: int32
    - name: '1'
      dtype: int32
  splits:
  - name: train
    num_bytes: 2219188.0
    num_examples: 2
  download_size: 2221784
  dataset_size: 2219188.0
configs:
- config_name: full
  data_files:
  - split: train
    path: full/train-*
- config_name: small
  data_files:
  - split: train
    path: small/train-*
  default: true
---

# 🧠 Open Humanoid Actuated Face Dataset

<p align="center">
  <img src="https://huggingface.co/datasets/iamirulofficial/Test2/resolve/main/imgesFace.png" alt="Sample Face Image" width="360"/>
</p>

## Dataset Summary
The **Open Humanoid Actuated Face Dataset** is designed for researchers working on  
facial‑actuation control, robotics, reinforcement learning, and human–computer interaction.

* **Origin** – collected during a reinforcement‑learning (RL) training loop whose objective was to reproduce human facial expressions.
* **Platform** – a modified **i2Head InMoov** humanoid head with a silicone skin.
* **Control** – **16 actuators** driving facial features and eyeballs.
* **Pairing** – each example contains the raw RGB image **and** the exact actuator angles that produced it.

---

## Dataset Structure

| Field           | Type          | Description                                                   |
|-----------------|---------------|---------------------------------------------------------------|
| `image`         | `Image`       | RGB capture of the humanoid face (resolution **[FILL_RES]**). |
| `actuated_angle`| `struct`      | 16 integer values (`"0"`, `"1"` .. so on)  |


### Actuator Index Reference

| Idx | Actuator                       | Idx | Actuator                |
|:---:|--------------------------------|:---:|-------------------------|
| 00  | Cheek – Left                   | 08  | Eyelid Upper – Right    |
| 01  | Cheek – Right                  | 09  | Eyelid Lower – Right    |
| 02  | Eyeball Sideways – Left        | 10  | Forehead – Right        |
| 03  | Eyeball Up/Down – Left         | 11  | Forehead – Left         |
| 04  | Eyelid Upper – Left            | 12  | Upper Nose              |
| 05  | Eyelid Lower – Left            | 13  | Eyebrow – Right         |
| 06  | Eyeball Up/Down – Right        | 14  | Jaw                     |
| 07  | Eyeball Sideways – Right       | 15  | Eyebrow – Left          |

---

### Actuator Mapping Images *(placeholders)*

| Full‑Face Map | Eye‑Only Map |
|:-------------:|:-----------:|
| <br><img src="https://huggingface.co/datasets/iamirulofficial/Test2/resolve/main/Screenshot%202025-05-02%20at%204.04.28%E2%80%AFPM.png" width="50%"/> | <br><img src="https://huggingface.co/datasets/iamirulofficial/Test2/resolve/main/Screenshot%202025-05-02%20at%204.03.56%E2%80%AFPM.png" width="50%"/> |


---

## Dataset Statistics

| Split | Samples | Size |
|-------|---------|------|
| **Train (full)**  | **[FILL_TOTAL]** | ≈ 105 GB |
| **Train (small)** | 2 | ≈ 2 GB |

*(Numbers shown are for the preview release — update as you add data.)*

---

## Usage Example

```python
from datasets import load_dataset, Image

# load the small subset
ds = load_dataset("iamirulofficial/OpenHumnoidDataset", name="small", split="train")
ds = ds.cast_column("image", Image())   # decode image bytes ➜ PIL.Image

img = ds[0]["image"]
angles = ds[0]["actuated_angle"]        # {'0': 90, '1': 20, ...}
img.show()
print(angles)
````

> **Tip** 
> For the full corpus use `name="full"` (may require `streaming=True` once the dataset grows).

---

## Data Collection & RL Setup

A detailed description of the RL pipeline, reward design, and actuator hardware will appear in our upcoming paper (in preparation, 2025). Briefly:

1. **Vision module** extracts target expression keypoints from live human video.
2. **Policy network** predicts 16 actuator set‑points.
3. **Real‑time reward** computes expression similarity + smoothness penalties.
4. Images & angle vectors are logged every *N* steps, forming this dataset.

---

## License

Released under the **MIT License** – free for commercial and non‑commercial use.

---

## Citation

```bibtex
@misc{amirul2025openhumanoidface,
  title   = {Open Humanoid Actuated Face Dataset},
  author  = {Amirul et al.},
  year    = {2025},
  url     = {https://huggingface.co/datasets/iamirulofficial/OpenHumnoidDataset}
}
```

---

## Acknowledgements

Big thanks to the **i2Head InMoov** community and everyone who helped engineer the silicone skin and actuator stack.