Implementation of our SIGGRAPH 2025 paper "Transformer IMU Calibrator: Dynamic On-body IMU Calibration for Inertial Motion Capture". Including network weights, training and evaluation scripts.
train.py: TIC Network training.
eval.py: Run our dynamic calibration on dataset and calculate OME, AME and R_G'G/R_BS Error.
Coming Soon.
The TIC dataset is available at https://www.dropbox.com/scl/fo/ggrvm8x2xjhu1m0pjomc9/ADClW3gbt4swggoulhndBKA?rlkey=bagguhrnze7fdvgr2toggce0v&st=p3fj8g1e&dl=0.
The data was collected from 5 subjects (s1~s5). For each subject, the dataset provides:
- acc.pt----Acceleration of 6 on-body IMU, calibrated by static calibration at begin.
- rot.pt----Orientation of 6 on-body IMU, calibrated by static calibration at begin.
- pose.pt----SMPL pose captured by NOKOV System (use optical tracker).
- trans.pt----Global body translation (location).
- drift.pt----Absolute coordinate drift of 6 on-body IMU.
- offset.pt----Measurement offset of 6 on-body IMU.
- acc_gt.pt----GT IMU acceleration captured by NOKOV System (use optical tracker).
*Note 1: IMU order: left forearm, right forearm, left lower leg, right lower leg, head, hip
*Note 2: All data are in SMPL frame.
Some of our codes are adapted from PIP. The SMPL_MALE model is download from https://smpl.is.tue.mpg.de/.