smplh.ipynb

export_mano.py

import os
import argparse
import pickle
import numpy as np


def csc_cols(m):
    col_start = m.indptr[:-1]
    col_end = m.indptr[1:]

    return np.concatenate(
        [
            np.full(end - start, i, dtype=int)
            for i, (start, end) in enumerate(zip(col_start, col_end))
        ]
    )


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("file", type=str)
    args = parser.parse_args()

    with open(args.file, "rb") as f:
        data = pickle.load(f)

    output = {}
    for key, data in data.iteritems():
        if "chumpy" in str(type(data)):
            output[key] = np.array(data)
        else:
            output[key] = data

    np.savez_compressed(
        os.path.splitext(args.file)[0],
        hands_components=output["hands_components"],
        hands_mean=output["hands_mean"],
        shapedirs=output["shapedirs"],
        v_template=output["v_template"],
        J_regressor_data=output["J_regressor"].data,
        J_regressor_rows=output["J_regressor"].indices,
        J_regressor_cols=csc_cols(output["J_regressor"]),
        kintree_table=output["kintree_table"],
        posedirs=output["posedirs"],
        weights=output["weights"],
        f=output["f"],
    )

export_smplh.py

import os
import argparse
import pickle
import numpy as np


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("file", type=str)
    args = parser.parse_args()

    with open(args.file, "rb") as f:
        data = pickle.load(f)

    output = {}
    for key, data in data.iteritems():
        if "chumpy" in str(type(data)):
            output[key] = np.array(data)
        else:
            output[key] = data

    np.savez_compressed(
        os.path.splitext(args.file)[0],
        shapedirs=output["shapedirs"],
        v_template=output["v_template"],
        J_regressor_data=output["J_regressor"].data,
        J_regressor_rows=output["J_regressor"].row,
        J_regressor_cols=output["J_regressor"].col,
        kintree_table=output["kintree_table"],
        posedirs=output["posedirs"],
        weights=output["weights"],
        f=output["f"],
    )

get_verts.py

import sys

sys.path.insert(0, ".")

from webuser.smpl_handpca_wrapper_HAND_only import load_model as load_model_mano
from webuser.smpl_handpca_wrapper import load_model
import numpy as np


def make_verts_mano(model, seed=0):
    m = load_model_mano(model)

    np.random.seed(seed)
    m.betas[:] = np.random.rand(m.betas.size) * 0.03
    # m.pose[:] = np.random.rand(m.pose.size) * 1.0
    m.pose[:3] = [0.0, 0.0, 0.0]
    m.pose[3:] = [
        -0.42671473,
        -0.85829819,
        -0.50662164,
        +1.97374622,
        -0.84298473,
        -1.29958491,
    ]

    return m.r


def make_verts_smplh(model, seed=0):
    m = load_model(model)

    np.random.seed(seed)
    m.betas[:] = np.random.rand(m.betas.size) * 0.03
    # m.pose[:] = np.random.rand(m.pose.size) * 1.0
    m.pose[:] = [
        -0.17192541,
        +0.36310464,
        +0.05572387,
        -0.42836206,
        -0.00707548,
        +0.03556427,
        +0.18696896,
        -0.22704364,
        -0.39019834,
        +0.20273526,
        +0.07125099,
        +0.07105988,
        +0.71328310,
        -0.29426986,
        -0.18284189,
        +0.72134655,
        +0.07865227,
        +0.08342645,
        +0.00934835,
        +0.12881420,
        -0.02610217,
        -0.15579594,
        +0.25352553,
        -0.26097519,
        -0.04529948,
        -0.14718626,
        +0.52724564,
        -0.07638319,
        +0.03324086,
        +0.05886086,
        -0.05683995,
        -0.04069042,
        +0.68593617,
        -0.75870686,
        -0.08579930,
        -0.55086359,
        -0.02401033,
        -0.46217096,
        -0.03665799,
        +0.12397343,
        +0.10974685,
        -0.41607569,
        -0.26874970,
        +0.40249335,
        +0.21223768,
        +0.03365140,
        -0.05243080,
        +0.16074013,
        +0.13433811,
        +0.10414972,
        -0.98688595,
        -0.17270103,
        +0.29374368,
        +0.61868383,
        +0.00458329,
        -0.15357027,
        +0.09531648,
        -0.10624117,
        +0.94679869,
        -0.26851003,
        +0.58547889,
        -0.13735695,
        -0.39952280,
        -0.16598853,
        -0.14982575,
        -0.27937399,
        +0.12354536,
        -0.55101035,
        -0.41938681,
        +0.52238684,
        -0.23376718,
        -0.29814804,
        -0.42671473,
        -0.85829819,
        -0.50662164,
        +1.97374622,
        -0.84298473,
        -1.29958491,
    ]

    return m.r


verts = {
    "left": make_verts_mano("models/MANO_LEFT.pkl"),
    "right": make_verts_mano("models/MANO_RIGHT.pkl"),
    "female": make_verts_smplh("models/SMPLH_female.pkl"),
    "male": make_verts_smplh("models/SMPLH_male.pkl"),
}
np.savez_compressed("verts", **verts)

smplh.ipynb