import numpy as np

rest_pelvis = np.matrix([[0.0000e+00,  0.0000e+00,  0.0000e+00],
                        [5.6144e-02, -9.4542e-02, -2.3475e-02],
                        [-5.7870e-02, -1.0517e-01, -1.6559e-02]])

# In inference stage, we only consider 24 joints
TO_24 = [
    0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 
    10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
    20, 21, 24, 26
]

SELECTED_JOINTS24 = [0, 1, 2, 3, 4, 
                    5, 6, 7, 8, 9, 
                    10, 11, 12, 13, 14, 
                    15, 16, 17, 18, 19, 
                    20, 21, 25, 40]

SELECTED_JOINT28 = [0, 1, 2, 3, 4, 
                   5, 6, 7, 8, 9, 
                   10, 11, 12, 13, 14, 
                   15, 16, 17, 18, 19, 
                   20, 21, 23, 24, 25, 
                   34, 40, 49]

rest_pelvis = np.matrix([[0.0000e+00,  0.0000e+00,  0.0000e+00],
                             [5.6144e-02, -9.4542e-02, -2.3475e-02],
                             [-5.7870e-02, -1.0517e-01, -1.6559e-02]])
pelvis_shift = [0.001144, -0.366919, 0.012666]

relaxed_hand_pose = np.array([0.11168, 0.04289, -0.41644,
                     0.10881, -0.06599, -0.75622,
                     -0.09639, -0.09092, -0.18846,
                     -0.1181, 0.05094, -0.52958,
                     -0.1437, 0.05524, -0.70486,
                     -0.01918, -0.09234, -0.33791,
                     -0.45703, -0.19628, -0.62546,
                     -0.21465, -0.066, -0.50689,
                     -0.36972, -0.06034, -0.07949,
                     -0.14187, -0.08585, -0.63553,
                     -0.30334, -0.05788, -0.63139,
                     -0.17612, -0.13209, -0.37335,
                     0.85096, 0.27692, -0.09155,
                     -0.49984, 0.02656, 0.05288,
                     0.53556, 0.04596, -0.27736,
                     0.11168, -0.04289, 0.41644,
                     0.10881, 0.06599, 0.75622,
                     -0.09639, 0.09092, 0.18846,
                     -0.1181, -0.05094, 0.52958,
                     -0.1437, -0.05524, 0.70486,
                     -0.01918, 0.09234, 0.33791,
                     -0.45703, 0.19628, 0.62546,
                     -0.21465, 0.066, 0.50689,
                     -0.36972, 0.06034, 0.07949,
                     -0.14187, 0.08585, 0.63553,
                     -0.30334, 0.05788, 0.63139,
                     -0.17612, 0.13209, 0.37335,
                     0.85096, -0.27692, 0.09155,
                     -0.49984, -0.02656, -0.05288,
                     0.53556, -0.04596, 0.27736]).astype(np.float32)