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.