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Official TensorFlow implementation of "PoseFix: Model-agnostic General Human Pose Refinement Network", CVPR 2019
| Date | Stars |
|---|---|
| 2026-07-24 | 330 |
| 2026-07-25 | 330 |
| 2026-07-28 | 330 |
| 2026-07-30 | 330 |
| 2026-08-06 | 330 |
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# PoseFix: Model-agnostic General Human Pose Refinement Network
[](https://paperswithcode.com/sota/multi-person-pose-estimation-on-coco?p=posefix-model-agnostic-general-human-pose)
<p align="center">
<b><i>PoseFix makes pose result of any methods better from a single '.json' file!</i></b>
</p>
<p align="center">
<img src="assets/qualitative1.PNG" width="400" height="250"> <img src="assets/qualitative2.PNG" width="400" height="250">
</p>
## News
We achieved top performance by refining the state-of-the-art ([HRNet](https://arxiv.org/abs/1902.09212), CVPR 2019). You can always make your results better!
## Introduction
This repo is official **[TensorFlow](https://www.tensorflow.org)** implementation of **[PoseFix: Model-agnostic General Human Pose Refinement Network (CVPR 2019)](https://arxiv.org/abs/1812.03595)** for **model-agnostic human pose refinement** from a single RGB image.
**What this repo provides:**
* [TensorFlow](https://www.tensorflow.org) implementation of [PoseFix: Model-agnostic General Human Pose Refinement Network](https://arxiv.org/abs/1812.03595).
* Flexible and simple code.
* Compatibility for most of the publicly available 2D multi-person pose estimation datasets including **[MPII](http://human-pose.mpi-inf.mpg.de/), [PoseTrack 2018](https://posetrack.net/), and [MS COCO 2017](http://cocodataset.org/#home)**.
* Human pose estimation visualization code (modified from [Detectron](https://github.com/facebookresearch/Detectron)).
## Dependencies
* [TensorFlow](https://www.tensorflow.org/)
* [CUDA](https://developer.nvidia.com/cuda-downloads)
* [cuDNN](https://developer.nvidia.com/cudnn)
* [Anaconda](https://www.anaconda.com/download/)
* [COCO API](https://github.com/cocodataset/cocoapi)
This code is tested under Ubuntu 16.04, CUDA 9.0, cuDNN 7.1 environment with two NVIDIA 1080Ti GPUs.
Python 3.6.5 version with Anaconda 3 is used for development.
## Directory
### Root
The `${POSE_ROOT}` is described as below.
```
${POSE_ROOT}
|-- data
|-- lib
|-- main
|-- tool
`-- output
```
* `data` contains data loading codes and soft links to images and annotations directories.
* `lib` contains kernel codes for 2d multi-person pose estimation system.
* `main` contains high-level codes for training or testing the network.
* `tool` contains dataset converter. `posetrack2coco_output.py` converts `posetrack` output files to `coco` format.
* `output` contains log, trained models, visualized outputs, and test result.
### Data
You need to follow directory structure of the `data` as below.
```
${POSE_ROOT}
|-- data
|-- |-- MPII
| `-- |-- input_pose
| | |-- name_of_input_pose.json
| | |-- test_on_trainset
| | | | -- result.json
| |-- annotations
| | |-- train.json
| | `-- test.json
| `-- images
| |-- 000001163.jpg
| |-- 000003072.jpg
|-- |-- PoseTrack
| `-- |-- input_pose
| | |-- name_of_input_pose.json
| | |-- test_on_trainset
| | | | -- result.json
| |-- annotations
| | |-- train2018.json
| | |-- val2018.json
| | `-- test2018.json
| |-- original_annotations
| | |-- train/
| | |-- val/
| | `-- test/
| `-- images
| |-- train/
| |-- val/
| `-- test/
|-- |-- COCO
| `-- |-- input_pose
| | |-- name_of_input_pose.json
| | |-- test_on_trainset
| | | | -- result.json
| |-- annotations
| | |-- person_keypoints_train2017.json
| | |-- person_keypoints_val2017.json
| | `-- image_info_test-dev2017.json
| `-- images
| |-- train2017/
| |-- val2017/
| `-- test2017/
`-- |-- imagenet_weights
| |-- resnet_v1_50.ckpt
| |-- resnet_v1_101.ckpt
| `-- resnet_v1_152.ckpt
```
* In Excerpt of 10,606 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
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