The code needs the following libraries:
- Python 3.7
- Anaconda
- PyTorch 1.4.0
We use the dataset of Human3.6M, LSP, MPII, COCO 2014, MPI-INF-3DHP and YOUTUBE Collection for training, and Human3.6M, LSP 3DPW and UPI_S1H for testing. We attach the google drive link of mask-rcnn results.
- Human 3.6M : http://vision.imar.ro/human3.6m/description.php
- Human 3.6M mask-rcnn results : https://drive.google.com/uc?id=12AkKZ6YSALNZgM4KZO3tcbescadln5Hz
├─[Human 3.6M path]
| ├─images
| S1_Directions_1.54138969_000001.jpg
| S1_Directions_1.54138969_000026.jpg
| ...
| ├─masks
| 0_S1_Directions_1.54138969_000001.jpg
| 0_S1_Directions_1.54138969_000026.jpg
| ...
├─[LSP path]
| ├─images
| im0001.jpg
| im0002.jpg
| ...
| ├─ masks
| 0_im0001.jpg
| 0_im0002.jpg
| ...
- MPII Images : http://human-pose.mpi-inf.mpg.de/#download
- MPII mask-rcnn results: https://drive.google.com/uc?id=1vU3_oOzOhGWN4M4kQApzx2jCrv3DIlAh
├─[MPII path]
| ├─images
| 000001163.jpg
| 000003072.jpg
| ...
| ├─masks
| 0_000001163.jpg
| 0_000003072.jpg
| ...
- COCO 2014 : https://cocodataset.org/#download
- COCO 2014 mask-rcnn results: https://drive.google.com/uc?id=190zdC5bG_WWzq4xXOhuCOy8NMfH_wfIv
├─[COCO path]
| ├─train2014
| COCO_train2014_000000000086.jpg
| COCO_train2014_000000000529.jpg
| ...
| ├─masks
| 0_COCO_train2014_000000000086.jpg
| 0_COCO_train2014_000000000529.jpg
| ...
- MPI_INF_3DHP : http://vcai.mpi-inf.mpg.de/3dhp-dataset/
- MPI_INF_3DHP mask-rcnn results: https://drive.google.com/uc?id=1XwK_TDb1YOrbw4YhJkC_2S3x_RdN_Dqz
├─[MPI_INF_3DHP path]
| ├─S1
| | ├─Seq1
| | | ├─imageFrames
| | | | ├─video_0
| frame_000001.jpg
| frame_000011.jpg
| ...
| | | ├─masks
| | | | ├─video_0
| 0_frame_000001.jpg
| 0_frame_000011.jpg
| ...
├─[3DPW path]
| ├─imageFiles
| | ├─downtown_arguing_00
| image_00000.jpg
| image_00001.jpg
| ...
├─[UPI_S1H path]
| ├─data
| | ├─lsp
| im0001_part_segmentation.png
| im0001_segmentation.png
| ...
- YOUTUBE Collection : https://drive.google.com/uc?id=1PDr4QU9B6rUzBqYySQn4MKu0-Aeeik-V
├─[YOUTUBE Collection path]
| ├─video1
| | ├─imageFiles
| frame000350.jpg
| frame000775.jpg
| ...
| | ├─masks
| 0_frame000350.jpg
| 0_frame000775.jpg
| ...
Download npz file, VIBE_data and other data.
source scripts/prepare_data.sh
├─SSPSE
│ ├─data
│ │ ├─dataset_extras
│ 3dpw_test.npz
| coco_2014_train.npz
| ...
| youtube_train.npz
We don't use LSP-extension.
python preprocessing.py
source scripts/pretrained.sh
Semi-supervised
python main.py --train 1 --output_dir semi_
Weakly-supervised
python main.py --train 1 --output_dir weakly_ --ignore_3d
Self-supervised
python main.py --train 1 --output_dir self_ --self_supervised
H36M
python main.py --train 0 --checkpoint results/semi/save_pth/best.pth --test_dataset h36m-p2
3DPW
python main.py --train 0 --checkpoint results/semi/save_pth/best.pth --test_dataset 3dpw
LSP
python main.py --train 0 --checkpoint results/semi/save_pth/best.pth --test_dataset lsp