[](https://arxiv.org/abs/2412.02254)     [](https://mirapurkrabek.github.io/ProbPose/)     [](LICENSE)

📋 Overview

ProbPose introduces a probabilistic framework for human pose estimation, focusing on reducing false positives by predicting keypoint presence probabilities and handling out-of-image keypoints. It also introduces the new Ex-OKS metric to evaluate models on false positive predictions.

Key contributions: - Presence probability concept that distinguishes keypoint presence from confidence - ProbPose: top-down model for out-of-image keypoints estimation - OKSLoss adapted for dense predictions in risk minimization formulation - Ex-OKS evaluation metric penalizing false positive keypoints - CropCOCO dataset for out-of-image and false positive keypoints evaluation

For more details, please visit our project website.

📢 News

• July 2025: exococotools PyPI package available
• June 2025: Live webcam demo branch available
• April 2025: Code is released
• March 2025: Paper accepted to CVPR 2025! 🎉

🚀 Installation

This project is built on top of MMPose. Please refer to the MMPose installation guide for detailed setup instructions.

Basic installation steps: ```bash

Clone the repository

git clone https://github.com/mirapurkrabek/ProbPose_code.git ProbPose/ cd ProbPose

Install your version of torch, torchvision, OpenCV and NumPy

pip install torch==2.1.2+cu121 torchvision==0.16.2+cu121 --extra-index-url https://download.pytorch.org/whl/cu121 pip install numpy==1.25.1 opencv-python==4.9.0.80

Install MMLibrary

pip install -U openmim mim install mmengine "mmcv==2.1.0" "mmdet==3.3.0" "mmpretrain==1.2.0"

Install dependencies

pip install -r requirements.txt pip install -e . ```

🎮 Demo

Single Image Demo

Run the following command to test ProbPose on a single image:

bash python demo/image_demo.py \ demo/resources/CropCOCO_single_example.jpg \ configs/body_2d_keypoint/topdown_probmap/coco/td-pm_ProbPose-small_8xb64-210e_coco-256x192.py \ path/to/pre-trained/weights.pth \ --out-file demo/results/CropCOCO_single_example.jpg \ --draw-heatmap

Expected result (click for full size):

Demo with MMDetection

For more complex scenarios with multiple people, use the MMDetection-based demo:

bash python demo/topdown_demo_with_mmdet.py \ demo/mmdetection_cfg/rtmdet_m_640-8xb32_coco-person.py \ https://download.openmmlab.com/mmpose/v1/projects/rtmpose/rtmdet_m_8xb32-100e_coco-obj365-person-235e8209.pth \ configs/body_2d_keypoint/topdown_probmap/coco/td-pm_ProbPose-small_8xb64-210e_coco-256x192.py \ path/to/pre-trained/weights.pth \ --input demo/resources/CropCOCO_multi_example.jpg \ --draw-bbox \ --output-root demo/results/ \ --draw-heatmap

Expected result (click for full size):

For more detailed information on demos and visualization options, please refer to the MMPose documentation.

📦 Pre-trained Models

Pre-trained models are available on VRG Hugging Face 🤗: - ProbPose-s weights

✂️ CropCOCO Dataset

The CropCOCO dataset is available on VRG Hugging Face 🤗.

For Ex-OKS and Ex-mAP evaluation, you can use cocoeval.py file which is a direct replacement for the original cocoeval.py file from xtcocotools. We plan to release Ex-mAP evaluation tool as a standalone package similar to xtcocotools.

📏 Ex-OKS Evaluation

Our Ex-OKS metric can be computed via the standalone exococotools package, which is fully backward-compatible with xtcocotools/pycocotools. Install and run it as a drop-in replacement:

bash pip install exococotools

For more details and advanced options, see the package website: https://github.com/MiraPurkrabek/exococotools

🗺️ Roadmap

• [ ] Add config and weights for DoubleProbmap model
• [x] Add out-of-image pose visualization
• [x] Add new package with Ex-OKS implementation --> exococotools
• [ ] Add ProbPose to MMPose library
• [x] Create HuggingFace demo

🙏 Acknowledgments

This project is built on top of MMPose. We would like to thank the MMPose team for their excellent work and support.

📝 Citation

If you find this work useful, please consider citing our paper:

bibtex @InProceedings{Purkrabek2025CVPR, author = {Purkrabek, Miroslav and Matas, Jiri}, title = {ProbPose: A Probabilistic Approach to 2D Human Pose Estimation}, booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)}, month = {June}, year = {2025}, pages = {27124-27133} }