Awesome

Multi-View Multi-Person 3D Pose Estimation with Plane Sweep Stereo

This repository includes the source code for our CVPR 2021 paper on multi-view multi-person 3D pose estimation. Please read our paper for more details at https://arxiv.org/abs/2104.02273. The project webpage is available here.

Bibtex:

@InProceedings{Lin_2021_CVPR,
    author    = {Lin, Jiahao and Lee, Gim Hee},
    title     = {Multi-View Multi-Person 3D Pose Estimation With Plane Sweep Stereo},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {11886-11895}
}

Environment

Our code is tested on

• Python 3.8.5
• PyTorch 1.6.0 & torchvision 0.7.0
• CUDA 11.2

Preparing Data

Download following data before using the code in this repository:

• Annotations and 2D pose predictions for the Campus and the Shelf datasets can be downloaded here. Credit to VoxelPose.
• Follow the instructions on the CMU Panoptic Github repo to download the annotations. 2D pose predictions can be downloaded here.
• Pre-trained models can be downloaded here.

The data should be organized as follows:

    ROOTDIR/
        └── data/
            └── Campus/
                └── actorsGT.mat
                └── calibration_campus.json
                └── pred_campus_maskrcnn_hrnet_coco.pkl
            └── Shelf/
                └── actorsGT.mat
                └── calibration_shelf.json
                └── pred_shelf_maskrcnn_hrnet_coco.pkl
            └── Panoptic/
                └── 160224_haggling1/
                └── 160226_haggling1/
                └── ...
                └── keypoints_train_results.json
                └── keypoints_validation_results.json
            └── panoptic_training_pose.pkl
        └── output/
            └── campus_synthetic/mvmppe/config/model_best_pretrained.pth.tar
            └── shelf_synthetic/mvmppe/config/model_best_pretrained.pth.tar
            └── panoptic/mvmppe/config/model_best_pretrained.pth.tar
        └── ...

Training and Inference

Below are the commands for training our model on different datasets.

The Campus dataset:

    python run/train.py --cfg configs/campus/config.yaml

The Shelf dataset:

    python run/train.py --cfg configs/shelf/config.yaml

The CMU Panoptic dataset:

    python run/train.py --cfg configs/panoptic/config.yaml

Below are the commands for performing inference with our pre-trained models.

The Campus dataset:

    python run/validate.py --cfg configs/campus/config.yaml -t pretrained

The Shelf dataset:

    python run/validate.py --cfg configs/shelf/config.yaml -t pretrained

The CMU Panoptic dataset:

    python run/validate.py --cfg configs/panoptic/config.yaml -t pretrained