PIP

Code originated from CVPR 2022 paper "Physical Inertial Poser (PIP): Physics-aware Real-time Human Motion Tracking from Sparse Inertial Sensors". See Project Page.

Usage

Install dependencies

PIP: Install correct version in requirements.txt

git clone https://github.com/TerryLiu007/pmp.git
pip install "torch-1.9.1+cpu-cp39-cp39-win_amd64.whl"
pip install "torchvision-0.10.1+cpu-cp39-cp39-win_amd64.whl"
pip install -r requirements.txt

RBDL:

git clone https://github.com/SlimeVRX/rbdl.git
python setup.py install

Run the evaluation

python evaluate.py

About the codes

In dynamics.py, there are many disabled options for the physics optimization. You can try different combinations of the energy terms by enabling the corresponding terms.
In Line ~44 in net.py:
```
self.dynamics_optimizer = PhysicsOptimizer(debug=False)
```
set debug=True to visualize the estimated motions using pybullet. You may need to clean the cached results and rerun the evaluate.py. (e.g., set flush_cache=True in evaluate() and rerun.)

In Line ~244 in dynamics.py:

if False:   # visualize GRF (no smoothing)
    p.removeAllUserDebugItems()
    for point, force in zip(collision_points, GRF.reshape(-1, 3)):
        p.addUserDebugLine(point, point + force * 1e-2, [1, 0, 0])

Enabling this to visualize the ground reaction force. (You also need to set debug=True as stated above.) Note that rendering the force lines can be very slow in pybullet.

The hyperparameters for the physics optimization are all in physics_parameters.json. If you set debug=True, you can adjust these parameters interactively in the pybullet window.