Edge Weight Prediction For Category-Agnostic Pose Estimation

By Or Hirschorn and Shai Avidan

This repo is the official implementation of "Edge Weight Prediction For Category-Agnostic Pose Estimation ".

Hugging Face Demo is now available!

🔔 News

• 12 December 2024 HuggingFace Demo is live.
• 25 November 2024 Initial Code Release

Introduction

Given only one example image and skeleton, our method refines the skeleton to enhance pose estimation on unseen categories.

Using our method, given a support image and skeleton we can refine the structure for better pose estimation on images from unseen categories.

Citation

Please consider citing our paper and GraphCape if you found our work useful:

@misc{hirschorn2024edgeweightpredictioncategoryagnostic,
      title={Edge Weight Prediction For Category-Agnostic Pose Estimation}, 
      author={Or Hirschorn and Shai Avidan},
      year={2024},
      eprint={2411.16665},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2411.16665}, 
}

@misc{hirschorn2023pose,
      title={A Graph-Based Approach for Category-Agnostic Pose Estimation}, 
      author={Or Hirschorn and Shai Avidan},
      year={2024},
      eprint={2311.17891},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2311.17891}, 
}

Getting Started

Docker [Recommended]

We provide a docker image for easy use. You can simply pull the docker image from docker hub, containing all the required libraries and packages:

docker pull orhir/edgecape
docker run --name edgecape -v {DATA_DIR}:/workspace/EdgeCape/EdgeCape/data/mp100 -it orhir/edgecape /bin/bash

Conda Environment

We train and evaluate our model on Python 3.8 and Pytorch 2.0.1 with CUDA 12.1.

Please first install pytorch and torchvision following official documentation Pytorch. Then, follow MMPose to install the following packages:

mmcv-full=1.7.2
mmpose=0.29.0

Having installed these packages, run:

python setup.py develop

MP-100 Dataset

Please follow Pose Anything to prepare the MP-100 dataset for training and evaluation, and organize the data structure properly.

Training

Training

To train the model, run:

python run.py --config [path_to_config_file]  --work_dir [path_to_work_dir]

Evaluation and Pretrained Models

Evaluation

The evaluation on a single GPU will take approximately 30 min.

To evaluate the pretrained model, run:

python test.py [path_to_config_file] [path_to_pretrained_ckpt]

Pretrained Models

You can download the pretrained models from following link.

Acknowledgement

Our code is based on code from:

• MMPose
• PoseAnything

License

This project is released under the Apache 2.0 license.