iamirulofficial/OpenHumnoidDataset

🧠 Open Humanoid Actuated Face Dataset

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.

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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

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Actuator Mapping Images (placeholders)

Full‑Face Map	Eye‑Only Map

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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.)

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Usage Example

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).

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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.

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License

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

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Citation

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

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Acknowledgements

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