ForcePose

This repo contains code and data for Learning to Estimate External Forces of Human Motion in Video, ACMMM 2022.

Update

• (10/08/2023) Released ForcePosev1.1
  • Include toe and heel keypoint detections using AlphaPose (Coco Wholebody+256x192+ResNet50)
  • Include center-of-pressure (cop) data points
  • Fix misalignment between RGB videos and force plate (and mocap data)

Data

Download data here (ForcePosev1.0)

ForcePose contains motion capture markers, coco detections, and force plate magnitudes for the following movements (triangulated COCO detections shown):

The ForcePose dataset contains 168 movements and 8 subjects (6 train, 2 validation).

124 trials/movements in train 44 trials/movements in validation

Each sample contains multiple camera views (up to 8), 2D pose predictions on each view, triangulated poses for each time step, and corresponding Ground Reaction Forces (GRFs). The RGB data is recorded 50 fps, the motion capture at 100 fps, and the force plates at 600 fps.

Data format (list): Each video/trial is a dict with the following keys:

- "subject" (string)
- "movement" (string)
- "frame_width" (int)
- "frame_height" (int)
- "total_frames" (int)
- "frames" (list)
        - "image_name" (string)
        - "image_index" (int)
        - "cam"_[x] (dict)
                - 'box' (bounding box - xtl,ytl,xbr,ybr)
                - 'keypoints' - 51 2D joints (17 joints x 3 [x,y,confidence]) (MSCOCO format)
        - "triangulated_pose" (list)
                - 17 triangulated joints (MSCOCO format)
- "mocap" (dict)
        - [marker_name] (list)
                - N frames, marker position  
- "grf" (dict) 
        - 'time' (list)
        - 'ground_force1_vx' (list)
        - 'ground_force1_vy' (list)
        - 'ground_force1_vz' (list)
        - 'ground_force2_vx' (list)
        - 'ground_force2_vy' (list)
        - 'ground_force2_vz' (list)
- "cop" (dict)
        - 'time' (list)
        - 'ground_force1_px' (list)
        - 'ground_force1_py' (list)
        - 'ground_force1_pz' (list)
        - 'ground_force2_px' (list)
        - 'ground_force2_py' (list)
        - 'ground_force2_pz' (list)

Code

System Requirements:

• Linux (Tested on Ubuntu 20.04)
• CUDA 11.x
• Python 3.8+
• PyTorch 1.7.1+

Creating a Conda Virtual Environment:

conda env create -f environment.yml
conda activate forcepose

Preparing data:

mv forcepose.tar.gz data/
cd data
tar -xvf forcepose.tar.gz
cd ..
python3 data/prepare_data_force_pose.py

This will create three files:

• data/data_2d_force_pose_pt_coco.npz
• data/data_3d_force_pose.npz
• data/data_2d_force_pose_gt.npz

Testing:

In our experiments, we optimize each modality for the lowest RMSE. Hence, the number of frames for pre-trained models will vary.

Download saved weights: mocap, coco_3d, coco_2d and extract inside of checkpoints directory.

frames	num_force_thresh	input_type	data_path	checkpoint_name
27	1	mocap	checkpoint/mocap	mocap_27frames.bin
27	2	mocap	checkpoint/mocap	mocap_27frames_t2.bin
43	1	3d	checkpoint/coco_3d	3d_43frames.bin
43	2	3d	checkpoint/coco_3d	3d_43frames_t2.bin
81	1	2d	checkpoint/coco_2d	2d_81frames.bin
81	2	2d	checkpoint/coco_2d	2d_81frames_t2.bin

Format:

python3 run_poseformer_force.py \ 
	-k pt_coco \
	-b 512 \
	-f $frames \
	--checkpoint $data_path \
	--num_force_thresh $num_force_thresh \
	--evaluate $checkpoint_name \
	--dataset force_pose \
	-str Subject1,Subject2,Subject3,Subject4,Subject5,Subject6 \
	-ste Subject7,Subject8 \
	--input_pose_type $input_type
	--exp_name eval_test

Example:

python3 run_poseformer_force.py \ 
	-k pt_coco \
	-b 512 \
	-f 81 \
	--num_force_thresh 2 \
	--checkpoint checkpoint/coco_2d/ \
	--evaluate 2d_81frames_t2.bin \
	--dataset force_pose \
	-str  Subject1,Subject2,Subject3,Subject4,Subject5,Subject6 \
	-ste  Subject7,Subject8 \
	--input_pose_type 2d \
	--exp_name eval_test

Training

Remove the evaluate flag to train a new model or replace with resume or pertained to fine-tune an existing model.

Acknowledgements

This code base was implemented from VideoPose3D and Poseformer.

Cite

@inproceedings{louis2022learning,
  title={Learning to Estimate External Forces of Human Motion in Video},
  author={Louis, Nathan and Corso, Jason J and Templin, Tylan N and Eliason, Travis D and Nicolella, Daniel P},
  booktitle={Proceedings of the 30th ACM International Conference on Multimedia},
  pages={3540--3548},
  year={2022}
}