code: def read_novel_caps(opt, num_caps, scene):
      novel_caps = []
      if (
          os.path.basename(opt.scene_dir) == "bike"
          and opt.motion_name == "jumpandroll"
      ):
          start_id = 25
          interval = 0.05
          for i in range(num_caps):
              temp = copy.deepcopy(scene.captures[start_id])
              temp.cam_pose.camera_center_in_world += (
                  interval * i * temp.cam_pose.right
              )
              novel_caps.append(temp)
      return novel_caps
  
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 1 for calling a global function with example usage: read_novel_caps(opt, num_caps, scene) and returns: novel_caps