Paper,

1. Clone this repo, and we'll call the directory that you cloned as ${POSE_ROOT}

2. Install dependencies.

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

   ${POSE_ROOT}/models
   └── pytorch
       └── imagenet
           ├── resnet152-b121ed2d.pth
           ├── resnet50-19c8e357.pth
           └── mobilenet_v2.pth.tar
   

   They can be downloaded from the following link: Pretrained Model Download

For TotalCapture dataset, please download from official site and follow zhezh/TotalCapture-Toolbox to process data.

We have no permission to redistribute this dataset. Please do not ask us for a copy.

For precalculated pictorial model pairwise term, please download from HERE, and save in data/pict.

To reproduce our results in the paper, please download the trained models from HERE.

python run/pose2d/valid.py \
--cfg experiments-local/totalcapture/res50_256_orn.yaml \
--model-file <path-to-your-download>/res50_256_final.pth.tar \
--gpus 0 --workers 1 \
--dataDir . --logDir log --modelDir output 

python run/pose3d/estimate.py \
--cfg experiments-local/totalcapture/res50_256_orn.yaml \
--withIMU 1 \
--dataDir . --logDir log --modelDir output

python run/pose2d/valid.py \
--cfg experiments-local/totalcapture/res50_256_nofusion.yaml \
--model-file <path-to-your-download>/res50_256_final.pth.tar \
--gpus 0 --workers 1 \
--dataDir . --logDir log --modelDir output 

Then,

python run/pose3d/estimate.py \
--cfg experiments-local/totalcapture/res50_256_nofusion.yaml \
--withIMU 0 \
--dataDir . --logDir log --modelDir output

Since our ORN and ORPSM has no learnable parameters, it can be conveniently appended to any 2D pose estimator. Thus training the SN backbone is sufficient.

python run/pose2d/train.py \
--cfg experiments-local/totalcapture/res50_256_nofusion.yaml \
--gpus 0 --workers 1 \
--dataDir . --logDir log --modelDir output

@inproceedings{zhe2020fusingimu,
  title={Fusing Wearable IMUs with Multi-View Images for Human Pose Estimation: A Geometric Approach},
  author={Zhang, Zhe and Wang, Chunyu and Qin, Wenhu and Zeng, Wenjun},
  booktitle = {CVPR},
  year={2020}
}

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