Function apply_rotation

motion_rep/retarget_motion.py:138–166

Update SMPL parameters based on the rotation matrix R. Args: smpl_params (dict): Dictionary containing SMPL parameters ('global_orient', 'transl', etc.). R (torch.Tensor): Rotation matrix of shape (3, 3). Returns: smpl_params (dict): Updated SMPL parameters.

(smpl_params, R)

    return R_inv

def apply_rotation(smpl_params, R):
    """
    Update SMPL parameters based on the rotation matrix R.

    Args:
        smpl_params (dict): Dictionary containing SMPL parameters ('global_orient', 'transl', etc.).
        R (torch.Tensor): Rotation matrix of shape (3, 3).

    Returns:
        smpl_params (dict): Updated SMPL parameters.
    """
    N = smpl_params['global_orient'].shape[0]  # Number of frames
    device = smpl_params['global_orient'].device

    # Convert global_orient from axis-angle to 3x3 matrix
    global_orient_mat = axis_angle_to_mat3x3(smpl_params['global_orient'].view(-1, 3))  # Shape: (N, 3, 3)

    # Adjust the global orientation by the computed rotation
    adjusted_global_orient_mat = torch.matmul(R[None,], global_orient_mat)  # Shape: (N, 3, 3)

    # Convert adjusted global_orient back to axis-angle
    smpl_params['global_orient'] = mat3x3_to_axis_angle(adjusted_global_orient_mat)  # Shape: (N, 3)

    # Adjust the translation by rotating
    smpl_params['transl'] += smplx_root.to(device)
    smpl_params['transl'] = torch.matmul(R[None,], smpl_params['transl'][..., None]).squeeze(-1)
    smpl_params['transl'] -= smplx_root.to(device)

    return smpl_params

def canonicalize_motion(smpl_params, joints, set_floor=False):
    # Get transformation and update smpl_params