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Revise README, add pypi action (#9)
* Revise README for consistency and additional details Updated README.md for improved clarity and formatting. * Add GitHub Actions workflow for PyPI release
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.github/workflows/release-pypi.yml

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name: Update internal release
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on:
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push:
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tags:
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- 'v*.*.*'
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pull_request:
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branches:
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- main
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types:
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- labeled
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- opened
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- edited
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- synchronize
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- reopened
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jobs:
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release:
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runs-on: ubuntu-latest
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steps:
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- name: Cache dependencies
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id: pip-cache
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uses: actions/cache@v4
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with:
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path: ~/.cache/pip
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key: ${{ runner.os }}-pip
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- name: Install dependencies
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run: |
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pip install --upgrade pip
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pip install wheel
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# NOTE(stes) see https://github.com/pypa/twine/issues/1216#issuecomment-2629069669
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pip install "packaging>=24.2"
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- name: Checkout code
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uses: actions/checkout@v3
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- name: Build and publish to Test PyPI
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if: ${{ (github.ref != 'refs/heads/main') && (github.event.label.name == 'release') }}
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env:
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TWINE_USERNAME: __token__
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TWINE_PASSWORD: ${{ secrets.TEST_PYPI_TOKEN }}
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run: |
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make dist
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ls dist/
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tar tvf dist/fmpose3d-*.tar.gz
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python3 -m twine upload --verbose --repository testpypi dist/*
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- name: Build and publish to PyPI
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if: ${{ github.event_name == 'push' }}
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env:
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TWINE_USERNAME: __token__
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TWINE_PASSWORD: ${{ secrets.PYPI_TOKEN }}
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run: |
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make dist
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ls dist/
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tar tvf dist/fmpose3d-*.tar.gz
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python3 -m twine upload --verbose dist/*

README.md

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# FMPose3D: monocular 3D Pose Estimation via Flow Matching
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# FMPose3D: monocular 3D pose estimation via flow matching
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![Version](https://img.shields.io/badge/python_version-3.10-purple)
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[![PyPI version](https://badge.fury.io/py/fmpose3d.svg)](https://badge.fury.io/py/fmpose3d)
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[![License: LApache 2.0](https://img.shields.io/badge/License-Apache2.0-blue.svg)](https://www.gnu.org/licenses/apach2.0)
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This is the official implementation of the approach described in the paper:
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> [**FMPose3D: monocular 3D Pose Estimation via Flow Matching**](xxx)
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> Ti Wang, Xiaohang Yu, Mackenzie Weygandt Mathis
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[**FMPose3D: monocular 3D Pose Estimation via Flow Matching**](xxx)
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Ti Wang, Xiaohang Yu, Mackenzie Weygandt Mathis
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<!-- <p align="center"><img src="./images/Frame 4.jpg" width="50%" alt="" /></p> -->
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<p align="center"><img src="./images/predictions.jpg" width="95%" alt="" /></p>
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## 🚀 TL;DR
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FMPose3D replaces slow diffusion models for monocular 3D pose estimation with fast Flow Matching, generating multiple plausible 3D poses via an ODE in just a few steps, then aggregates them using a reprojection-based Bayesian module (RPEA) for accurate predictions, achieving state-of-the-art results on human and animal 3D pose benchmarks.
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## News!
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- [X] Feb 2026: FMPose3D is code and arXiv paper is released - check out the demos here or on our [project page](https://xiu-cs.github.io/FMPose3D/)
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- [X] Feb 2026: FMPose3D code and arXiv paper is released - check out the demos here or on our [project page](https://xiu-cs.github.io/FMPose3D/)
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- [ ] Planned: This method will be integrated into [DeepLabCut](https://www.mackenziemathislab.org/deeplabcut)
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## Installation

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