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https://github.com/facebookresearch/eft
visualization code for 3D human body annotation by EFT (Exemplar Fine-tuning)
https://github.com/facebookresearch/eft
Last synced: about 2 months ago
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visualization code for 3D human body annotation by EFT (Exemplar Fine-tuning)
- Host: GitHub
- URL: https://github.com/facebookresearch/eft
- Owner: facebookresearch
- License: other
- Created: 2020-06-22T23:33:32.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2022-01-16T09:57:12.000Z (over 2 years ago)
- Last Synced: 2024-03-04T15:35:52.518Z (4 months ago)
- Language: Python
- Size: 1.18 MB
- Stars: 358
- Watchers: 18
- Forks: 35
- Open Issues: 15
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Lists
- awesome-3D-human - EFT
README
# [Exemplar Fine-Tuning for 3D Human Pose Fitting Towards In-the-Wild 3D Human Pose Estimation](https://arxiv.org/abs/2004.03686)
This repository contains pseudo-GT 3D human pose data produced by [Exemplar Fine-Tuning (EFT)](https://arxiv.org/abs/2004.03686) method, published in [3DV 2021](https://3dv2021.surrey.ac.uk/). The 3D pose data is in the form of [SMPL](https://smpl.is.tue.mpg.de/) parameters, and this can be used as a supervision to train a 3D pose estimation algiritm (e.g., [SPIN](https://github.com/nkolot/SPIN) or [HMR](https://github.com/akanazawa/hmr)).
We found that our EFT dataset is sufficient to build a model that is comparable to the previous SOTA algorithms without using any other indoor 3D pose dataset. See our [paper](https://arxiv.org/abs/2004.03686) for more details.
This repository also contains the pre-trained 3D pose estimation model trained with our EFT dataset and monocular motion capture demo tools. See [README_bodymocap](README_bodymocap.md).
## News:
- We have released the EFT fitting codes. See the [README_run_eft](https://github.com/facebookresearch/eft/blob/eft_fit/README_run_eft.md) file:
- We have released [FrankMocap](https://github.com/facebookresearch/frankmocap) by which you can obtain both 3D body+hand outputs. The body module is the same as this repository's model. We encourage to use [FrankMocap](https://github.com/facebookresearch/frankmocap) for body pose estimation.
## Installing Requirements
It is convenient and safe to use conda environment
```
conda create -n venv_eft python=3.6
conda activate venv_eft
pip install -r requirements.txt
```
## Download EFT Fitting data (json formats)
This repository only provides corresponding SMPL parameters for public 2D keypoint datasets (such as [COCO](https://cocodataset.org/), [MPII](http://human-pose.mpi-inf.mpg.de/)). You need to download images from the original dataset website.
Run the following script to download our EFT fitting data:
```
sh scripts/download_eft.sh
```
- The EFT data will be saved in ./eft_fit/(DB_name).json. Each json file contains a version EFT fitting for a public dataset.
- See [Data Format](docs/README_dataformat.md) for details
- Currently available EFT fitting outputs:
|Dataset Name | SampleNum | Manual Filtering | File Name |
|---------------| -----------| ---------------- |-------------------------- |
|COCO2014-12kp | 28344 | No | COCO2014-Part-ver01.json |
|COCO2014-6kp | 79051 | No | COCO2014-All-ver01.json |
|COCO2014-Val | 10510 | Yes | COCO2014-Val-ver10.json |
|MPII | 14361 | No | MPII_ver01.json |
|PoseTrack | 28856 | No | PoseTrack_ver01.json |
|LSPet-Train | 2946 | Yes | LSPet_ver01.json |
|LSPet-Test | 2433 | Yes | LSPet_test_ver10.json |
|OCHuman-Train | 2495 | Yes | OCHuman_train_ver10.json |
|OCHuman-Test | 1783 | Yes | OCHuman_test_ver10.json |
- COCO2014-All-ver01.json: [COCO](https://cocodataset.org/#home) 2014 training set by selecting the samples 6 keypoints or more keypoints are annotated.
- COCO2014-Part-ver01.json: [COCO](https://cocodataset.org/#home) 2014 training set by selecting the sample that 12 limb keypoints or more are annotated.
- COCO2014-Val-ver10.json: [COCO](https://cocodataset.org/#home) 2014 val set.
- MPII_ver01.json : [MPII](http://human-pose.mpi-inf.mpg.de/) Keypoint Dataset
- PoseTrack_ver01.json : [PoseTrack](https://posetrack.net/) Dataset by selecting the sample that 12 limb keypoints or more are annotated.
- LSPet: [LSPet](https://sam.johnson.io/research/lspet.html) Dataset
- OCHuman : [OCHuman](https://github.com/liruilong940607/OCHumanApi) Dataset
- Note that the number of samples are fewer than the original sample numbers in each DB, since we automatically (or manually) filtered out bad samples
- Manual Filtering: Manual quality check and filtering is done to keep high quality results only. See paper for details
### Download Other Required Data
- SMPL Model (Neutral model: basicModel_neutral_lbs_10_207_0_v1.0.0.pkl):
- Download in the original [website](http://smplify.is.tue.mpg.de/login). You need to register to download the SMPL data.
- Put the file in: ./extradata/smpl/basicModel_neutral_lbs_10_207_0_v1.0.0.pkl
- Copy the smpl pkl file to a different name (SMPL_NEUTRAL.pkl). You need both files:
```
cp ./extradata/smpl/basicModel_neutral_lbs_10_207_0_v1.0.0.pkl ./extradata/smpl/SMPL_NEUTRAL.pkl
```
- Densepose (optional, for Densepose rendering):
- Run the following script
```
sh scriptsdownload_dp_uv.sh
```
- Files are saved in ./extradata/densepose_uv_data/
## Download Images from Original Public DB website
- [COCO](https://cocodataset.org/#home): [2014 Training set](http://images.cocodataset.org/zips/train2014.zip)
- [MPII](http://human-pose.mpi-inf.mpg.de/): [Download Link](https://datasets.d2.mpi-inf.mpg.de/andriluka14cvpr/mpii_human_pose_v1.tar.gz)
- [LSPet](https://sam.johnson.io/research/lspet.html): [Download Link](http://datasets.d2.mpi-inf.mpg.de/hr-lspet/hr-lspet.zip)
## Visualize EFT Fitting Results
- See [README_eft_vis](README_eft_vis.md)
## Run EFT Fitting by yourself
- See [README_run_eft](README_run_eft.md)
## Model Zoo
- We also share pre-trained models trained with diverse dataset.
- Plaese see "scripts/download_model_zoo.sh"
## Motion Capture Demo
- See [README_bodymocap](README_bodymocap.md)
## Citation
```
@inproceedings{joo2020eft,
title={Exemplar Fine-Tuning for 3D Human Pose Fitting Towards In-the-Wild 3D Human Pose Estimation},
author={Joo, Hanbyul and Neverova, Natalia and Vedaldi, Andrea},
booktitle={3DV},
year={2020}
}
```
## License
[CC-BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/legalcode).
See the [LICENSE](LICENSE) file.
## References
The body mocap code is a modified version of [SPIN](https://github.com/nkolot/SPIN), and the majority of this code is borrowed from it.