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):
Models
config | MPJPE (mm) | model & log |
45.3 | ||
33.1 | ||
30.4 | ||
19 | ㅤ |
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)
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 datasetTraining
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 for non-augmented models and extract under
outs/
- Make sure
outs/epipolar/keypoint_h36m_fixed/visualizations/h36m/output_1.pklexists.
- Use
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
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
# 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:
