prediction = np.load(args.viz_input)
rot = np.array([0.14070565, -0.15007018, -0.7552408, 0.62232804],
dtype=np.float32) # Example value taken from h36m
prediction = camera_to_world(prediction, R=rot,
t=0) # Invert camera transformation
prediction[:, :, 2] -= np.min(
prediction[:, :, 2]
) # We don't have the trajectory, but at least we can rebase the height
anim_output = {'Inference': prediction}
skeleton = Skeleton(
parents=[
-1, 0, 1, 2, 3, 4, 0, 6, 7, 8, 9, 0, 11, 12, 13, 14, 12, 16, 17, 18,
19, 20, 19, 22, 12, 24, 25, 26, 27, 28, 27, 30
],
joints_left=[6, 7, 8, 9, 10, 16, 17, 18, 19, 20, 21, 22, 23],
joints_right=[1, 2, 3, 4, 5, 24, 25, 26, 27, 28, 29, 30, 31])
skeleton.remove_joints(
[4, 5, 9, 10, 11, 16, 20, 21, 22, 23, 24, 28, 29, 30, 31])
skeleton._parents[11] = 8
skeleton._parents[14] = 8
render_animation(anim_output,
skeleton,
24,
3000,
70.0,
args.viz_output,
viewport=(1000, 1002))
[3.48356, -0., -0.],
[0., 0., 0.],
[0.71526, -0., -0.],
[0., 0., 0.],
[0., 0., 0.],
[-3.1366, 1.37405, -0.40465],
[-5.2419, -0., -0.],
[-3.44417, -0., -0.],
[0., 0., 0.],
[-0.62253, -0., -0.],
[0., 0., 0.],
],
parents=[
-1, 0, 1, 2, 3, 4, 0, 6, 7, 8, 9, 0, 11, 12, 13,
14, 15, 13, 17, 18, 19, 20, 21, 20, 13, 24, 25, 26,
27, 28, 27
],
joints_left=[6, 7, 8, 9, 10, 21, 22, 23, 24, 25],
joints_right=[1, 2, 3, 4, 5, 16, 17, 18, 19, 20])
dataset_path = '/storage/quaternet/dataset_locomotion.npz'
long_term_weights_path = '/artifacts/weights_long_term.bin'
dataset = MocapDataset(dataset_path, skeleton_cmu, fps=120)
# Remove useless joints, from both the skeleton and the dataset
skeleton_cmu.remove_joints([13, 21, 23, 28, 30], dataset)
dataset.mirror()
dataset.compute_euler_angles('yzx')
dataset.downsample(4)
