| --- |
| task_categories: |
| - keypoint-detection |
| --- |
| |
| # Dataset Card for SIMSPINE |
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| ## Dataset Summary |
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| 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. |
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| 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. |
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| 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 |
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| The dataset **does not include any original Human3.6M data**. |
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| Instead, it provides **additional annotations aligned with Human3.6M frames**, which can be combined with the original dataset after users obtain it independently. |
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| ## 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 |
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| ## Dataset Structure |
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| TBA. |
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| ## Dataset Creation |
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| ### Source Dataset |
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| SIMSPINE is derived from the **Human3.6M dataset**. |
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| Human3.6M contains: |
| - synchronized multi-view videos |
| - motion capture ground truth |
| - 3D human joint annotations (17 joints) |
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| SIMSPINE does **not redistribute any of this data**. |
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| Users must obtain Human3.6M independently from the official source: https://vision.imar.ro/human3.6m/ |
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| ### Annotation Pipeline |
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| The SIMSPINE annotations were generated using the following pipeline: |
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| 1. **Human3.6M keypoints and videos** were used to estimate additional anatomical landmarks. |
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| 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). |
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| 3. A **biomechanical spine model** was constructed using OpenSim. |
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| 4. The pseudo-labels were used to simulate **15 anatomically plausible spine-related joints**. |
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| 5. The resulting joints were aligned with the Human3.6M coordinate system and frames. |
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| The final dataset therefore consists of **simulated spine joints consistent with the original Human3.6M motion sequences**. |
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| ## Dataset Statistics |
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| The dataset spans the same subjects, actions, and frames as Human3.6M. |
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| More details TBA. |
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| ## Intended Uses |
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| SIMSPINE is intended for academic research in human pose estimation and biomechanics. |
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| Typical use cases include: |
| - training spine-aware pose estimation models |
| - evaluating spine reconstruction methods |
| - studying spinal motion patterns |
| - improving full-body pose estimation |
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| ## Out-of-Scope Uses |
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| The dataset should not be used for: |
| - clinical diagnosis |
| - medical decision making |
| - commercial products without permission |
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| ## Licensing Information |
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| SIMSPINE is released under the [**SIMSPINE Academic Research License**](LICENSE). |
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| Key conditions: |
| - Academic use only |
| - No redistribution of the dataset |
| - Proper citation required |
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| SIMSPINE is a **derived dataset from Human3.6M**. |
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| Users must also comply with the **Human3.6M license agreement**, available at: |
| https://vision.imar.ro/human3.6m/ |
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| SIMSPINE does **not include**: |
| - Human3.6M images |
| - Human3.6M videos |
| - Human3.6M motion capture data |
| - Human3.6M joint annotations |
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| Users must independently obtain Human3.6M to use this dataset. |
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| ## Citation |
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| 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}, |
| } |
| ``` |
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| 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} |
| } |
| ``` |
|
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| ## Acknowledgements |
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| This dataset builds upon the Human3.6M dataset created by: |
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| Catalin Ionescu, Dragos Papava, Vlad Olaru, and Cristian Sminchisescu. |
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| ## Contact |
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| For questions regarding SIMSPINE: |
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| Muhammad Saif Ullah Khan |
| Augmented Vision Group |
| German Research Center for Artificial Intelligence (DFKI) |
| Kaiserslautern, Germany |
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| Email: muhammad_saif_ullah.khan@dfki.de |
