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https://github.com/ethanhe42/epipolar-transformers
Epipolar Transformers (best paper award, CVPR 2020 workshop)
https://github.com/ethanhe42/epipolar-transformers
3d 3dposeestimation deep-learning pose-estimation pytorch
Last synced: 27 days ago
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Epipolar Transformers (best paper award, CVPR 2020 workshop)
- Host: GitHub
- URL: https://github.com/ethanhe42/epipolar-transformers
- Owner: ethanhe42
- License: mit
- Created: 2020-03-30T00:09:57.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2024-03-09T16:04:49.000Z (3 months ago)
- Last Synced: 2024-04-13T06:55:01.251Z (2 months ago)
- Topics: 3d, 3dposeestimation, deep-learning, pose-estimation, pytorch
- Language: Jupyter Notebook
- Homepage: https://yihui.dev/epipolar-transformers
- Size: 6.48 MB
- Stars: 403
- Watchers: 22
- Forks: 36
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Lists
- Awesome-SLAM - Epipolar Transformers
README
# Epipolar Transformers
[GitHub - yihui-he/epipolar-transformers: Epipolar Transformers (CVPR 2020)](https://github.com/yihui-he/epipolar-transformers)
[Epipolar Transformers](https://arxiv.org/abs/2005.04551)
[Yihui He](http://yihui-he.github.io/), [Rui Yan](https://github.com/Yre), [Katerina Fragkiadaki](https://www.cs.cmu.edu/~katef/), [Shoou-I Yu](https://sites.google.com/view/shoou-i-yu/home) (Carnegie Mellon University, Facebook Reality Labs)
**CVPR 2020**, **[CVPR workshop Best Paper Award](https://vuhcs.github.io/)**
Oral presentation and human pose demo videos ([playlist](https://www.youtube.com/playlist?list=PLkz610aVEqV-f4Ws0pH0e8Nm_2wTGI1yP)):
[https://www.youtube.com/embed/nfb0kfVWjcs](https://www.youtube.com/embed/nfb0kfVWjcs)
[https://www.youtube.com/embed/ig5c-qTaYkg](https://www.youtube.com/embed/ig5c-qTaYkg)
## Models
| config | MPJPE (mm) | model & log |
| --- | --- | --- |
| https://www.notion.soconfigs/benchmark/keypoint_h36m.yaml | 45.3 | https://github.com/yihui-he/epipolar-transformers/releases/download/outputs/outs.benchmark.keypoint_h36m_afterfix.zip |
| https://www.notion.soconfigs/epipolar/keypoint_h36m_zresidual_fixed.yaml | 33.1 | https://github.com/yihui-he/epipolar-transformers/releases/download/outputs/outs.epipolar.keypoint_h36m_fixed.zip |
| https://www.notion.soconfigs/epipolar/keypoint_h36m_zresidual_aug.yaml | 30.4 | https://github.com/yihui-he/epipolar-transformers/releases/download/outputs/outs.epipolar.keypoint_h36m_fixed_aug.zip |
| https://www.notion.soconfigs/epipolar/keypoint_h36m_resnet152_384_pretrained_8gpu.yaml | 19 | |
We also provide 2D to 3D lifting network implementations for these two papers:
- [3D Hand Shape and Pose from Images in the Wild](https://arxiv.org/abs/1902.03451), CVPR 2019
- `configs/lifting/img_lifting_rot_h36m.yaml` (Human 3.6M)
- `configs/lifting/img_lifting_rot.yaml` (RHD)
- [Learning to Estimate 3D Hand Pose from Single RGB Images](http://openaccess.thecvf.com/content_ICCV_2017/papers/Zimmermann_Learning_to_Estimate_ICCV_2017_paper.pdf), ICCV 2017
- `configs/lifting/lifting_direct_h36m.yaml` (Human 3.6M)
- `configs/lifting/lifting_direct.yaml` (RHD)
## Setup
### Requirements
Python 3, pytorch > 1.2+ and pytorch < 1.4
```
pip install -r requirements.txtconda install pytorch cudatoolkit=10.0 -c pytorch
```
### Pretrained weights download
```
mkdir outscd datasets/bash get_pretrained_models.sh
```
Please follow the instructions in `datasets/README.md` for preparing the dataset
### Training
```
python main.py --cfg path/to/configtensorboard --logdir outs/
```
### Testing
Testing with latest checkpoints
```
python main.py --cfg configs/xxx.yaml DOTRAIN False
```
Testing with weights
```
python main.py --cfg configs/xxx.yaml DOTRAIN False WEIGHTS xxx.pth
```
## Visualization
### Epipolar Transformers Visualization
- Download the [output pkls](https://github.com/yihui-he/epipolar-transformers/releases/download/outputs/outs.epipolar.keypoint_h36m_fixed.visualizations.zip) for non-augmented models and extract under `outs/`
- Make sure `outs/epipolar/keypoint_h36m_fixed/visualizations/h36m/output_1.pkl` exists.
- Use `[scripts/vis_hm36_score.ipynb](https://github.com/yihui-he/epipolar-transformers/blob/master/scripts/vis_hm36_score.ipynb)`
- To select a point, click on the reference view (upper left), the source view along with corresponding epipolar line, and the peaks for different feature matchings are shown at the bottom left.
### Human 3.6M input visualization
```bash
python main.py --cfg configs/epipolar/keypoint_h36m.yaml DOTRAIN False DOTEST False EPIPOLAR.VIS True VIS.H36M True SOLVER.IMS_PER_BATCH 1
python main.py --cfg configs/epipolar/keypoint_h36m.yaml DOTRAIN False DOTEST False VIS.MULTIVIEWH36M True EPIPOLAR.VIS True SOLVER.IMS_PER_BATCH 1
```
### Human 3.6M prediction visualization
[https://www.youtube.com/embed/ig5c-qTaYkg](https://www.youtube.com/embed/ig5c-qTaYkg)
```bash
# generate images
python main.py --cfg configs/epipolar/keypoint_h36m_zresidual_fixed.yaml DOTRAIN False DOTEST True VIS.VIDEO True DATASETS.H36M.TEST_SAMPLE 2
# generate images
python main.py --cfg configs/benchmark/keypoint_h36m.yaml DOTRAIN False DOTEST True VIS.VIDEO True DATASETS.H36M.TEST_SAMPLE 2
# use https://github.com/yihui-he/multiview-human-pose-estimation-pytorch to generate images for ICCV 19
python run/pose2d/valid.py --cfg experiments-local/mixed/resnet50/256_fusion.yaml
# set test batch size to 1 and PRINT_FREQ to 2
# generate video
python scripts/video.py --src outs/epipolar/keypoint_h36m_fixed/video/multiview_h36m_val/
```
## Citing Epipolar Transformers
If you find Epipolar Transformers helps your research, please cite the paper:
```
@inproceedings{epipolartransformers,
title={Epipolar Transformers},
author={He, Yihui and Yan, Rui and Fragkiadaki, Katerina and Yu, Shoou-I},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={7779--7788},
year={2020}
}
```
## FAQ
Please create a new issue:
[Issues · yihui-he/epipolar-transformers](https://github.com/yihui-he/epipolar-transformers/issues)