mirror of https://github.com/open-mmlab/mmpose
83 lines
2.6 KiB
Markdown
83 lines
2.6 KiB
Markdown
# Pose Anything: A Graph-Based Approach for Category-Agnostic Pose Estimation
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## [Paper](https://arxiv.org/pdf/2311.17891.pdf) | [Project Page](https://orhir.github.io/pose-anything/) | [Official Repo](https://github.com/orhir/PoseAnything)
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By [Or Hirschorn](https://scholar.google.co.il/citations?user=GgFuT_QAAAAJ&hl=iw&oi=ao)
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and [Shai Avidan](https://scholar.google.co.il/citations?hl=iw&user=hpItE1QAAAAJ)
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## Introduction
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We present a novel approach to CAPE that leverages the inherent geometrical
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relations between keypoints through a newly designed Graph Transformer Decoder.
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By capturing and incorporating this crucial structural information, our method
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enhances the accuracy of keypoint localization, marking a significant departure
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from conventional CAPE techniques that treat keypoints as isolated entities.
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## Citation
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If you find this useful, please cite this work as follows:
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```bibtex
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@misc{hirschorn2023pose,
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title={Pose Anything: A Graph-Based Approach for Category-Agnostic Pose Estimation},
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author={Or Hirschorn and Shai Avidan},
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year={2023},
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eprint={2311.17891},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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```
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## Getting Started
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📣 Pose Anything is available on OpenXLab now. [\[Try it online\]](https://openxlab.org.cn/apps/detail/orhir/Pose-Anything)
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### Install Dependencies
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We recommend using a virtual environment for running our code.
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After installing MMPose, you can install the rest of the dependencies by
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running:
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```
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pip install timm
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```
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### Pretrained Weights
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The full list of pretrained models can be found in
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the [Official Repo](https://github.com/orhir/PoseAnything).
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## Demo on Custom Images
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***A bigger and more accurate version of the model - COMING SOON!***
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Download
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the [pretrained model](https://drive.google.com/file/d/1RT1Q8AMEa1kj6k9ZqrtWIKyuR4Jn4Pqc/view?usp=drive_link)
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and run:
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```
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python demo.py --support [path_to_support_image] --query [path_to_query_image] --config configs/demo_b.py --checkpoint [path_to_pretrained_ckpt]
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```
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***Note:*** The demo code supports any config with suitable checkpoint file.
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More pre-trained models can be found in the official repo.
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## Training and Testing on MP-100 Dataset
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**We currently only support demo on custom images through the MMPose repo.**
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**For training and testing on the MP-100 dataset, please refer to
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the [Official Repo](https://github.com/orhir/PoseAnything).**
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## Acknowledgement
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Our code is based on code from:
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- [CapeFormer](https://github.com/flyinglynx/CapeFormer)
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## License
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This project is released under the Apache 2.0 license.
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