GitHub - yihui-he/epipolar-transformers: Epipolar Transformers (CVPR 2020)

Epipolar Transformers

Yihui He, Rui Yan, Katerina Fragkiadaki, Shoou-I Yu (Carnegie Mellon University, Facebook Reality Labs)

CVPR 2020, CVPR workshop Best Paper Award

Oral presentation and human pose demo videos (playlist):

https://www.youtube.com/embed/nfb0kfVWjcs

https://www.youtube.com/embed/ig5c-qTaYkg

We also provide 2D to 3D lifting network implementations for these two papers:

• 3D Hand Shape and Pose from Images in the Wild, 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, ICCV 2017
  • configs/lifting/lifting_direct_h36m.yaml (Human 3.6M)
  • configs/lifting/lifting_direct.yaml (RHD)

Python 3, pytorch > 1.2+ and pytorch < 1.4

pip install -r requirements.txtconda install pytorch cudatoolkit=10.0 -c pytorch

mkdir outscd datasets/bash get_pretrained_models.sh

Please follow the instructions in datasets/README.md for preparing the dataset

python main.py --cfg path/to/configtensorboard --logdir outs/

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

• Download the output pkls 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.

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

https://www.youtube.com/embed/ig5c-qTaYkg

# 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/

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}
}

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