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Research code for metric depth estimation in periocular VR imagery using UE MetaHuman-generated data.
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# DeepMetricEye
[中文](README.zh-CN.md) | English
[](LICENSE)
[](https://doi.org/10.1109/ISMAR59233.2023.00058)
[](https://arxiv.org/abs/2311.07235)
[](DPDG_Environment/)
[](DepthEstimationModel/)
Research code and data-generation tooling for **DeepMetricEye: Metric Depth Estimation in Periocular VR Imagery**.
The repository contains the Dynamic Periocular Data Generation (DPDG) environment and a periocular depth-estimation model for reconstructing metric eye-region geometry from monocular VR headset imagery.
## Why This Exists
Eye-oriented cameras in modern VR headsets can observe pupil and periocular features, but their 2D outputs are not enough for metric measurements such as pupil diameter, periocular deformation, or light-stimulus evaluation. DeepMetricEye bridges that gap with:
- a lightweight monocular depth-estimation model for periocular imagery,
- a UE MetaHuman-based synthetic data-generation environment,
- RGB/depth training pairs for rapid experimentation,
- a reproducible starting point for VR eye-health and XR sensing research.
## Repository Layout
| Path | Purpose |
| --- | --- |
| [`DPDG_Environment/`](DPDG_Environment/) | Unreal Engine 5.2 MetaHuman environment for synthetic periocular image and depth-map generation. |
| [`DepthEstimationModel/`](DepthEstimationModel/) | PyTorch/Jupyter implementation of the periocular depth-estimation model and a minimal sample dataset. |
| [`docs/data-access.md`](docs/data-access.md) | Public data notes, sensitive-data policy, and author contact for restricted raw scans. |
| [`CITATION.cff`](CITATION.cff) | Machine-readable citation metadata for GitHub, Zotero, and citation managers. |
## Quick Start
### Depth-Estimation Model
```bash
cd DepthEstimationModel
python -m venv .venv
source .venv/bin/activate
pip install torch torchvision pillow matplotlib scipy tqdm numpy
jupyter notebook model.ipynb
```
The included `train_data_minimal/` directory is a small RGB/depth sample for verifying the training and inference pipeline. It is not the full research dataset.
### DPDG Environment
1. Install Unreal Engine 5.2.
2. Download the DPDG package from the link documented in [`DPDG_Environment/README.md`](DPDG_Environment/README.md).
3. Open `HumanDataset.uproject`.
4. Configure MetaHuman identity, VR headset geometry, camera pose, lighting, and output directories.
5. Export synchronized periocular RGB images and ground-truth depth maps.
## Research Links
- Project page: <https://yitongsun.com/deepmetriceye>
- Paper DOI: <https://doi.org/10.1109/ISMAR59233.2023.00058>
- arXiv: <https://arxiv.org/abs/2311.07235>
- Code repository: <https://github.com/sunyitong/DeepMetricEye>
- Data access notes: [`docs/data-access.md`](docs/data-access.md)
## Data Access
The full experimental data contains sensitive facial information and is not distributed directly through this repository. See [`docs/data-access.md`](docs/data-access.md) for the public sample dataset, restricted data policy, and contact process.
## Citation
If this repository helps your research, please cite the paper:
```bibtex
@inproceedings{Sun_2023,
title={DeepMetricEye: Metric Depth Estimation in Periocular VR Imagery},
DOI={10.1109/ISMAR59233.2023.00058},
booktitle={2023 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)},
publisher={IEEE},
author={Sun, Yitong and Zhou, Zijian and Diels, Cyriel and Asadipour, Ali},
year={2023},
pages={434--443}
}
```
## Contributing
Issues and pull requests are welcome, especially for reproducibility fixes, documentation improvements, dataset-loader cleanExcerpt of 4,225 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:a49b9fef61c5398d, topic:computer-vision, desc:depth estimation, readme:depth estimation
matched fp:a49b9fef61c5398d, topic:pytorch