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Self-Supervised Learning of 3D Human Pose using Multi-view Geometry (CVPR2019)
| Date | Stars |
|---|---|
| 2026-07-24 | 608 |
| 2026-07-25 | 608 |
| 2026-07-28 | 608 |
| 2026-07-30 | 608 |
| 2026-08-06 | 608 |
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[](https://paperswithcode.com/sota/3d-human-pose-estimation-human36m?p=self-supervised-learning-of-3d-human-pose)
# Self-Supervised Learning of 3D Human Pose using Multi-view Geometry (CVPR2019) [[project page](https://mkocabas.github.io/epipolarpose.html)]
## Introduction
This is a pytorch implementation of
[*Self-Supervised Learning of 3D Human Pose using Multi-view Geometry*](https://arxiv.org/abs/1903.02330) paper.
> [**Self-Supervised Learning of 3D Human Pose using Multi-view Geometry**](https://arxiv.org/abs/1903.02330),
> [Muhammed Kocabas](http://user.ceng.metu.edu.tr/~e2270981/)\*, [Salih Karagoz](https://salihkaragoz.github.io/)\*,
[Emre Akbas](http://user.ceng.metu.edu.tr/~emre/),
> *IEEE Computer Vision and Pattern Recognition, 2019* (\*equal contribution)
In this work, we present **_EpipolarPose_**, a self-supervised learning method for
3D human pose estimation, which does not need any 3D ground-truth data or camera extrinsics.
During training, EpipolarPose estimates 2D poses from multi-view images, and then, utilizes epipolar geometry
to obtain a 3D pose and camera geometry which are subsequently used to train a 3D pose estimator.
In the test time, it only takes an RGB image to produce a 3D pose result. Check out [`demo.ipynb`](demo.ipynb) to
run a simple demo.
Here we show some sample outputs from our model on the Human3.6M dataset.
For each set of results we first show the input image, followed by the ground truth,
fully supervised model and self supervised model outputs.
<p align="center"><img src="https://i.imgur.com/jZph53h.png" width="100%" alt=""/></p>
### Video Demo
<p align="center"><a target=_blank href="http://www.youtube.com/watch?v=lkXBiKRfRDw"><img src="http://img.youtube.com/vi/lkXBiKRfRDw/0.jpg" width="50%" alt="" /></a></p>
## Overview
- `scripts/`: includes training and validation scripts.
- `lib/`: contains data preparation, model definition, and some utility functions.
- `refiner/`: includes the implementation of _refinement unit_ explained in the paper Section 3.3.
- `experiments/`: contains `*.yaml` configuration files to run experiments.
- `sample_images/`: images from Human3.6M dataset to run demo notebook.
## Requirements
The code is developed using python 3.7.1 on Ubuntu 16.04. NVIDIA GPUs ared needed to train and test.
See [`requirements.txt`](requirements.txt) or [`environment.yml`](environment.yml) for other dependencies.
## Quick start
### Installation
1. Install pytorch >= v1.0.0 following [official instructions](https://pytorch.org/).
_Note that if you use pytorch's version < v1.0.0, you should follow the instructions at
<https://github.com/Microsoft/human-pose-estimation.pytorch> to disable cudnn's implementation of
BatchNorm layer. We encourage you to use higher pytorch's version(>=v1.0.0)_
2. Clone this repo, and we will call the directory that you cloned as `${ROOT}`
3. Install dependencies using `pip`.
```
pip install -r requirements.txt
```
or create a new `conda` env:
```
conda env create -f environment.yml
```
4. Download annotation files from [GoogleDrive](https://drive.google.com/open?id=147AlIWRv9QDmp5pGjwG2yMWEG_2-E2ai)
(150 MB) as a zip file under `${ROOT}` folder. Run below commands to unzip them.
```
unzip data.zip
rm data.zip
```
5. Finally prepare your workspace by running:
```bash
mkdir output
mkdir models
```
Optionally you can download pretrained weights using the links in the below table. You can put them under `models`
directory. At the end, your directory tree should like this.
```
${ROOT}
├── data/
├── experiments/
├── lib/
├── models/
├── output/
├── refiner/
├── sample_images/
├── scripts/
├── demo.ipynb
├── README.md
└── requirements.txt
```
6. YeExcerpt of 11,511 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:9c8f10b4bbab4e98, topic:computer-vision, readme:computer vision, readme:pose estimation
matched fp:9c8f10b4bbab4e98, topic:pytorch