By Or Hirschorn and Shai Avidan
We present a novel approach to CAPE that leverages the inherent geometrical relations between keypoints through a newly designed Graph Transformer Decoder. By capturing and incorporating this crucial structural information, our method enhances the accuracy of keypoint localization, marking a significant departure from conventional CAPE techniques that treat keypoints as isolated entities.
If you find this useful, please cite this work as follows:
@misc{hirschorn2023pose,
title={Pose Anything: A Graph-Based Approach for Category-Agnostic Pose Estimation},
author={Or Hirschorn and Shai Avidan},
year={2023},
eprint={2311.17891},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
📣 Pose Anything is available on OpenXLab now. [Try it online]
We recommend using a virtual environment for running our code. After installing MMPose, you can install the rest of the dependencies by running:
pip install timm
The full list of pretrained models can be found in the Official Repo.
A bigger and more accurate version of the model - COMING SOON!
Download the pretrained model and run:
python demo.py --support [path_to_support_image] --query [path_to_query_image] --config configs/demo_b.py --checkpoint [path_to_pretrained_ckpt]
Note: The demo code supports any config with suitable checkpoint file. More pre-trained models can be found in the official repo.
We currently only support demo on custom images through the MMPose repo.
For training and testing on the MP-100 dataset, please refer to the Official Repo.
Our code is based on code from:
This project is released under the Apache 2.0 license.