---
task_categories:
- keypoint-detection
---

# Dataset Card for SIMSPINE

## Dataset Summary

SIMSPINE is a dataset of **additional 3D spine keypoint annotations derived from the Human3.6M dataset**. The dataset provides **15 simulated spine-related joints** aligned with Human3.6M motion capture sequences.

The annotations are generated using a biomechanical simulation pipeline built on **OpenSim**, combined with pseudo-labeling derived from the original Human3.6M keypoints and video frames.

SIMSPINE extends the standard Human3.6M skeleton by providing additional spine-related joints that enable:
- fine-grained modeling of spinal motion
- biomechanical analysis
- improved human pose estimation for spine-aware models
- evaluation of spine reconstruction algorithms

The dataset **does not include any original Human3.6M data**.

Instead, it provides **additional annotations aligned with Human3.6M frames**, which can be combined with the original dataset after users obtain it independently.

## Supported Tasks
SIMSPINE can be used for:
- 2D human pose estimation
- 3D human pose estimation
- spine pose estimation
- human motion analysis
- biomechanical modeling
- 2D-to-3D pose lifting research
- human pose reconstruction

## Dataset Structure

TBA.

## Dataset Creation

### Source Dataset

SIMSPINE is derived from the **Human3.6M dataset**.

Human3.6M contains:
- synchronized multi-view videos
- motion capture ground truth
- 3D human joint annotations (17 joints)

SIMSPINE does **not redistribute any of this data**.

Users must obtain Human3.6M independently from the official source: https://vision.imar.ro/human3.6m/

### Annotation Pipeline

The SIMSPINE annotations were generated using the following pipeline:

1. **Human3.6M keypoints and videos** were used to estimate additional anatomical landmarks.

2. **Pseudo-labels** were generated for intermediate anatomical points along the torso using 2D detectors from [SpinePose](https://openaccess.thecvf.com/content/CVPR2025W/CVSPORTS/html/Khan_Towards_Unconstrained_2D_Pose_Estimation_of_the_Human_Spine_CVPRW_2025_paper.html).

3. A **biomechanical spine model** was constructed using OpenSim.

4. The pseudo-labels were used to simulate **15 anatomically plausible spine-related joints**.

5. The resulting joints were aligned with the Human3.6M coordinate system and frames.

The final dataset therefore consists of **simulated spine joints consistent with the original Human3.6M motion sequences**.

## Dataset Statistics

The dataset spans the same subjects, actions, and frames as Human3.6M.

More details TBA.

## Intended Uses

SIMSPINE is intended for academic research in human pose estimation and biomechanics.

Typical use cases include:
- training spine-aware pose estimation models
- evaluating spine reconstruction methods
- studying spinal motion patterns
- improving full-body pose estimation

## Out-of-Scope Uses

The dataset should not be used for:
- clinical diagnosis
- medical decision making
- commercial products without permission

## Licensing Information

SIMSPINE is released under the [**SIMSPINE Academic Research License**](LICENSE).

Key conditions:
- Academic use only
- No redistribution of the dataset
- Proper citation required

SIMSPINE is a **derived dataset from Human3.6M**.

Users must also comply with the **Human3.6M license agreement**, available at:
 https://vision.imar.ro/human3.6m/

SIMSPINE does **not include**:
- Human3.6M images
- Human3.6M videos
- Human3.6M motion capture data
- Human3.6M joint annotations

Users must independently obtain Human3.6M to use this dataset.

## Citation

If you use SIMSPINE, please cite:
```bibtex
@inproceedings{khan2026simspine,
  author = {Khan, Muhammad Saif Ullah and Stricker, Didier},
  title = {SIMSPINE: A Biomechanics-Aware Simulation Framework for 3D Spine Motion Annotation and Benchmarking},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2026},
}
```

You must also cite the Human3.6M papers:
```bibtex
@article{h36m_pami,
  author = {Ionescu, Catalin and Papava, Dragos and Olaru, Vlad and Sminchisescu, Cristian},
  title = {Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year = {2014}
}

@inproceedings{IonescuSminchisescu11,
  author = {Catalin Ionescu, Fuxin Li, Cristian Sminchisescu},
  title = {Latent Structured Models for Human Pose Estimation},
  booktitle = {International Conference on Computer Vision},
  year = {2011}
}
```

## Acknowledgements

This dataset builds upon the Human3.6M dataset created by:

Catalin Ionescu, Dragos Papava, Vlad Olaru, and Cristian Sminchisescu.

## Contact

For questions regarding SIMSPINE:

Muhammad Saif Ullah Khan
Augmented Vision Group
German Research Center for Artificial Intelligence (DFKI)
Kaiserslautern, Germany

Email: muhammad_saif_ullah.khan@dfki.de