gaitplotlib

A way to map from SMPL/SMPL-H parameters to plottable coordinates/meshes in 3D space

This library is supposed to make it easier to convert from SMPL-H/SMPL parameters, which have become the de facto pose representation in the literature, into points and meshes that can be plotted in different visualisation packages.

Install

Requirements:

• PyTorch
• human_body_prior (I fixed version the required version at 7f0a4b3)
• chumpy (the only reason this has to be installed is because the SMPL/SMPL-H model pickle files contain chumpy array objects, these are turned into numpy arrays in my unpacking script)
• mediapy for loading, manipulating and rendering videos/animations
• matplotlib although the points and meshes could be plotted in other frameworks
• smplx (only to provide annotations for the SMPL-H joint names)

I think it might be legally necessary to sign up on the SMPLX project page in order to use smplx and human_body_prior?

Install with pip from github:

pip install git+https://github.com/gngdb/gaitplotlib.git

How to use

Download Body Models

First, obtain body model archives from MPI, either: SMPL or SMPL-H. Make an account and download the archives.

Unpack Body Models

A console script is provided to unpack the body models:

unpack_body_models <directory containing archives> <smpl/smplh>

This will do two things:

1. Unpack the body model archives and put them in the format matching what SMPL-X expects (because I originally planned to use that code)
2. Create symbolic links in ~/.config/gaitplotlib to find the unpacked body models later when using them for plotting

Make pose angles plottable

SMPL* pose data is defined by a vector of pose angles. Assuming you have such a vector, you can map it to a plottable form

> params = plottable(data['poses'])
> print(params)
vertices:torch.Size([601, 6890, 3])joints: torch.Size([601, 52, 3])
faces: torch.Size([13776, 3])
skeleton: torch.Size([51, 2])

params is an object storing vertices, joints, faces and skeleton as properties and it has a __getitem__ method to index poses in the sequence, which are output as dictionaries:

> print({k:v.shape for k,v in params[0].items()}){'vertices': torch.Size([6890, 3]), 'joints': torch.Size([52, 3]), 'faces': torch.Size([13776, 3]), 'skeleton': torch.Size([51, 2])}

# smplh
with tempfile.TemporaryDirectory() as tmpdirname:
    shutil.unpack_archive("sample_data/sample.tar.bz2", extract_dir=tmpdirname)
    for npz_loc in (Path(tmpdirname) / "sample" / "subdir").glob("*.npz"):
        if "amass" in npz_loc.name:
            print(f"loading from {npz_loc}")
            data = np.load(npz_loc)

loading from /tmp/tmpf5m0ezk6/sample/subdir/amass_sample.npz

# smplh
import matplotlib.pyplot as plt

plt.style.use("ggplot")

for d in range(3):
    dims_to_plot = [i for i in range(3) if i != d]
    joints, skeleton = params[0]["joints"], params.skeleton
    j = joints[:, dims_to_plot]
    plt.scatter(*j.T, color="r", s=0.2)
    # dim limits must match or figure will be distorted
    xl, yl = plt.xlim(), plt.ylim()
    l = (min(xl[0], yl[0]), max(xl[1], yl[1]))
    plt.xlim(*l)
    plt.ylim(*l)
    for bone in skeleton:
        a = j[bone[0]]
        b = j[bone[1]]
        x, y = list(zip(a, b))
        plt.plot(x, y, color="r", alpha=0.5)
    plotted_dims = "".join(["x", "y", "z"][i] for i in dims_to_plot)
    plt.show()