IMPORTANT NOTE: I've completely bastardized this. Not for external use yet. Get original code.

ORIGINAL HEADER: # [IJCV Article] AdaFuse: Adaptive Multiview Fusion for Accurate Human Pose Estimation in the Wild

Resources

Paper: (arXiv:2010.13302)

Occlusion-Person Dataset: (GitHub)

Install & Data Preparation

Clone this repo, and install the dependencies.

git clone https://github.com/zhezh/adafuse-3d-human-pose.git adafuse
cd adafuse
conda install pytorch==1.2.0 torchvision==0.4.0 -c pytorch
pip install -r requirements.txt

We use Pytorch 1.2.0 with Ubuntu 16.04 LTS (CUDA 10.1). Other versions, e.g. Pytorch > 1.0, CUDA > 9.0 and Ubuntu 18, should also be applicable.

The adafuse directory will be referred as {POSE_ROOT}.

Pretrained Models

Download pytorch pretrained models. Please download them under ${POSE_ROOT}/models, and make them look like this:

${POSE_ROOT}/models
└── pytorch
    ├── adafuse
    │   ├── h36m_4view.pth.tar
    │   └── occlusion_person_8view.pth.tar
    ├── pose_backbone
    │   ├── h36m_4view_d87025.pth.tar
    │   └── occlusion_person_8view_c20e11.tar
    ├── pose_coco (Optional)
    │   ├── pose_resnet_152_384x288.pth.tar
    │   ├── pose_resnet_50_256x192.pth.tar

They can be downloaded from the this link

Human3.6M

Please follow CHUNYUWANG/H36M-Toolbox to prepare the data.

Note that we have NO permission to redistribute the Human3.6M data. Please do NOT ask us for a copy of Human3.6M dataset.

Occlusion-Person

Please follow zhezh/occlusion_person to prepare the data.

MPII (Optional)

For MPII data, please refer to microsoft/multiview-human-pose-estimation-pytorch.

Evaluate

Make sure you are in the {POSE_ROOT} directory.

Human3.6M

python run/adafuse/adafuse_main.py --cfg experiments/h36m/h36m_4view.yaml --evaluate true

Occlusion-Person

python run/adafuse/adafuse_main.py --cfg experiments/occlusion_person/occlusion_person_8view.yaml --evaluate true

Results

Human3.6M

MPJPE summary: j3d_NoFuse 22.94
MPJPE summary: j3d_HeuristicFuse 21.02
MPJPE summary: j3d_ScoreFuse 20.14
MPJPE summary: j3d_ransac 21.77
MPJPE summary: j3d_AdaFuse 19.54

Occlusion-Person

MPJPE summary: j3d_NoFuse 48.16
MPJPE summary: j3d_HeuristicFuse 18.02
MPJPE summary: j3d_ScoreFuse 14.97
MPJPE summary: j3d_ransac 15.40
MPJPE summary: j3d_AdaFuse 12.56

Train

Human3.6M

python run/adafuse/adafuse_main.py --cfg experiments/h36m/h36m_4view.yaml --runMode train

Occlusion-Person

python run/adafuse/adafuse_main.py --cfg experiments/occlusion_person/occlusion_person_8view.yaml --runMode train

Train 2D pose backbone

We provide pre-trained 2D pose backbone parameters in the models/pytorch/pose_backbone directory, if you would like to train them by yourself, please follow below instructions.

Firstly, follow previous instructions to prepare the Pretrained Models and MPII dataset which are labeled as "Optional".

Then,

For Human3.6M (MPII is needed to augment Human3.6M training data),

python run/pose2d/train.py --cfg experiments/pose2d/h36m.yaml

For Occlusion-Person,

python run/pose2d/train.py --cfg experiments/pose2d/occlusion_person.yaml

Finally, replace the attribute NETWORK/PRETRAINED in AdaFuse yaml config (e.g. in experiments/h36m/h36m_4view.yaml) with newly obtained model path (e.g. output/h36m_res50.pth.tar).

Citation

@article{zhang2020adafuse,
      title={AdaFuse: Adaptive Multiview Fusion for Accurate Human Pose Estimation in the Wild}, 
      author={Zhe Zhang and Chunyu Wang and Weichao Qiu and Wenhu Qin and Wenjun Zeng},
      year={2020},
      journal={IJCV},
      publisher={Springer},
      pages={1--16},
}