code: def read_novel_caps(opt, num_caps, scene):
      novel_caps = []
      if (
          os.path.basename(opt.scene_dir) == "seattle"
          and opt.motion_name == "dance_together"
      ):
          for i in range(num_caps):
              cap = copy.deepcopy(scene.captures[20])
              ellipse_a = 0.15
              ellipse_b = 0.05
              x_offset = (
                  cap.cam_pose.right
                  * ellipse_a
                  * np.cos((i / num_caps) * (4 * np.pi))
              )
              y_offset = (
                  cap.cam_pose.up
                  * ellipse_b
                  * np.sin((i / num_caps) * (4 * np.pi))
              )
              cap.cam_pose.camera_center_in_world = (
                  cap.cam_pose.camera_center_in_world
                  + x_offset
                  + y_offset
              )
              novel_caps.append(cap)
      return novel_caps
  
file_path: ml-neuman/render_gathering.py
imports: import os
  import argparse
  import copy
  import joblib
  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