mRI:

Data repo for mRI: Multi-modal 3D Human Pose Estimation Dataset using mmWave, RGB-D, and Inertial Sensors

Demo in Project page: https://sizhean.github.io/mri

Dataset download link in google drive

New dataset download link with RGB videos included and a new readme file

Please note that we need to process the part of the data (camera related modalities) due to privacy-preserving protocol, which might delay the data release. The dataset (including camera realted modalities) will be fully open-sourced soon.

After unzip the dataset_release.zip, the folder structure should be like this:

${ROOT}
|-- raw_data
|   |-- imu
|   |-- eaf_file
|   |-- radar
|   |-- unixtime
|   |-- videolabels
|-- aligned_data
|   |-- imu
|   |-- radar
|   |-- pose_labels
|-- features
|   |-- imu
|   |-- radar
|-- model
|   |-- imu
|   |   |-- results
|   |   |-- *.pkl
|   |-- mmWave
|   |   |-- results
|   |   |-- *.pkl

Load cpl file The .cpl file is essentially pickle file, to read them, use:

import pickle
file = pickle.load(open('.../file_path/XXX.cpl', 'rb'))

raw_data folder contains all raw_data before synchronization. It includes imu raw data, radar raw data, eaf annotations, unix timestamp from camera, and videolabels generated from the eaf file.

aligned_data folder contains all data after temporal alignment. It includes imu data, radar data, and the pose_labels. pose_labels for each subject contain following information:

'2d_l_avail_frames': available frames for 2d human detection, left camera
'2d_r_avail_frames': available frames for 2d human detection, right camera
'camera_matrix': camera parameters
'gt_avail_frames': available frames for 3d human joints ground truth
'imu_avail_frames': available frames for imu-estimated keypoints
'imu_est_kps': imu-estimated keypoints
'naive_gt_kps': naive triangulation keypoints
'pose_2d_l': human 2d keypoints from left camera
'pose_2d_r': human 2d keypoints from right camera
'radar_avail_frames': available frames for radar-estimated keypoints
'radar_est_kps': radar-estimated keypoints
'refined_gt_kps': refined triangulation keypoints ground truth
'rgb_avail_frames': available frames for rgb-estimated keypoints
'rgb_est_kps': rgb-estimated keypoints
'video_label': video action labels

feature folder contains imu, radar features for deep learning models. The features are generated from the synced data.

Dimension of the radar feature is (frames, 14, 14, 5). The final 5 means x, y, z-axis coordinates, Doppler velocity, and intensity.

Dimension of the radar feature is (frames, 6, 12). 6 is the number of IMUs and 12 is flattened 3x3 rotation and 3 accelerations.

model folder contains the pretrained model .pkl files and and results.