---
license: other
pretty_name: ProPose
tags:
- human-pose-estimation
- prosthesis
- residual-limbs
- 2d-pose-estimation
- benchmark
---

# ProPose Dataset

This repository hosts the dataset for **ProPose: Topology-Unified 2D Pose Estimation across Intact, Residual and Prosthetic Limbs**.

ProPose is a 2D human pose estimation benchmark designed for inclusive pose estimation across intact, residual, and prosthetic limbs. It follows the proposed **Omni-Pose** annotation protocol, which represents biological, prosthetic, and physically absent keypoints within a unified topology.

## Dataset File

The dataset is released as a single ZIP archive:

ProPose.zip

After downloading and extracting the archive, the dataset is organized into training, validation, and test splits.

## Dataset Structure

ProPose.zip contains the following structure:

    ProPose/
    ├── train/
    │   ├── images/
    │   └── propose_train.json
    ├── val/
    │   ├── images/
    │   └── propose_val.json
    └── test/
        ├── images/
        └── propose_test.json

Each split contains:

- `images/`: image files used for training, validation, or testing.
- `propose_*.json`: the corresponding pose annotation file following the Omni-Pose annotation format.

## Annotation Format

The annotation files include 2D keypoint coordinates and semantic keypoint type labels. Each keypoint is associated with a semantic type:

- `Bio`: biological keypoint.
- `Pros`: prosthetic keypoint.
- `Abs`: physically absent keypoint.

This design enables unified pose representation for intact limbs, residual limbs, and prosthetic limbs.

## Code and Documentation

The official code, training configuration, evaluation scripts, and additional documentation are available at:

https://github.com/SoraLink/ProPose

## Citation

If you use this dataset or code, please cite our paper:

    @inproceedings{qi2026propose,
      title={Topology-Unified 2D Pose Estimation across Intact, Residual and Prosthetic Limbs},
      author={Qi, Tianye and Zhang, Tengyue and Ying, Jiaying and Zhu, Tianqing and Yu, Xin},
      booktitle={European Conference on Computer Vision},
      year={2026}
    }