code: def read_human_poses(opt, scene):
      # read mocap data(already in SMPL format)
      mocap_path, start_idx, end_idx, skip = get_mocap_path(opt)
      motions = np.load(mocap_path)
      poses = motions["poses"][start_idx:end_idx:skip]
      poses = poses[:, :72]
      poses[:, 66:] = 0
      trans = motions["trans"][start_idx:end_idx:skip]
      beta = scene.smpls[0]["betas"]
  
      body_model = SMPL(
          "./data/smplx/smpl",
          gender="neutral",
          device=torch.device("cpu"),
      )
  
      # read manual alignment
      manual_trans, manual_rot, manual_scale = get_manual_alignment(opt)
      M_R = transformations.euler_matrix(*manual_rot)
      M_S = np.eye(4)
      M_S[:3, :3] *= manual_scale
      M_T = transformations.translation_matrix(manual_trans)
      T_mocap2scene = M_T[None] @ M_S[None] @ M_R[None]
  
      # 大 pose
      da_smpl = np.zeros_like(np.zeros((1, 72)))
      da_smpl = da_smpl.reshape(-1, 3)
      da_smpl[1] = np.array([0, 0, 1.0])
      da_smpl[2] = np.array([0, 0, -1.0])
      da_smpl = da_smpl.reshape(1, -1)
  
      raw_verts = []
      Ts = []
      for i, p in enumerate(poses):
          # transformations from T-pose to mocap pose(random scale)
          _, T_t2mocap = body_model.verts_transformations(
              return_tensor=False,
              poses=p[None],
              betas=beta[None],
              transl=trans[i][None],
          )
          # transform mocap data to scene space
          T_t2scene = T_mocap2scene @ T_t2mocap
          # T-pose to Da-pose
          _, T_t2da = body_model.verts_transformations(
              return_tensor=False, poses=da_smpl, betas=beta[None]
          )
          # Da-pose to scene space
          T_da2scene = T_t2scene @ np.linalg.inv(T_t2da)
          # Da-pose verts
          temp_static_verts, _ = body_model(
              return_tensor=False,
              return_joints=True,
              poses=da_smpl,
              betas=beta[None],
          )
          # verts in scene
          verts = np.einsum(
              "BNi, Bi->BN",
              T_da2scene,
              ray_utils.to_homogeneous(temp_static_verts),
          )[:, :3].astype(np.float32)
          raw_verts.append(verts)
          Ts.append(T_da2scene)
      return raw_verts, Ts
  
file_path: ml-neuman/render_reposing.py
imports: import os
  import argparse
  import copy
  import imageio
  import torch
  import numpy
  from geometry import transformations
  from models import human_nerf
  from utils import render_utils, utils, ray_utils
  from data_io import neuman_helper
  from options import options
  from models.smpl import SMPL
label_desc: Use image node_id 4 for calling a global function with example usage: read_human_poses(opt, scene) and returns: raw_verts,  Ts