diff --git a/lib/sdf.py b/lib/sdf.py
index c44cca3..a7a8487 100644
--- a/lib/sdf.py
+++ b/lib/sdf.py
@@ -90,9 +90,9 @@ def eval_grid_octree(coords, eval_func,
 
     sdf = np.zeros(resolution)
 
-    notprocessed = np.zeros(resolution, dtype=np.bool)
+    notprocessed = np.zeros(resolution, dtype=bool)
     notprocessed[:-1,:-1,:-1] = True
-    grid_mask = np.zeros(resolution, dtype=np.bool)
+    grid_mask = np.zeros(resolution, dtype=bool)
 
     reso = resolution[0] // init_resolution
 
diff --git a/working_copy_of_PIFuHD_Demo_aug24.ipynb b/working_copy_of_PIFuHD_Demo_aug24.ipynb
new file mode 100644
index 0000000..46d235d
--- /dev/null
+++ b/working_copy_of_PIFuHD_Demo_aug24.ipynb
@@ -0,0 +1,916 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/SHREYAS1188/pifuhdShreyas/blob/main/working_copy_of_PIFuHD_Demo_aug24.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eclLG4xlJRIE"
+      },
+      "source": [
+        "# PIFuHD Demo: https://shunsukesaito.github.io/PIFuHD/\n",
+        "\n",
+        "![](https://shunsukesaito.github.io/PIFuHD/resources/images/pifuhd.gif)\n",
+        "\n",
+        "Made by [![Follow](https://img.shields.io/twitter/follow/psyth91?style=social)](https://twitter.com/psyth91)\n",
+        "\n",
+        "To see how the model works, visit the project repository.\n",
+        "\n",
+        "[![GitHub stars](https://img.shields.io/github/stars/facebookresearch/pifuhd?style=social)](https://github.com/facebookresearch/pifuhd)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wmFdsTvLKtBO"
+      },
+      "source": [
+        "## Note\n",
+        "Make sure that your runtime type is 'Python 3 with GPU acceleration'. To do so, go to Edit > Notebook settings > Hardware Accelerator > Select \"GPU\"."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1TfPAtL4CyZw"
+      },
+      "source": [
+        "## More Info\n",
+        "- Paper: https://arxiv.org/pdf/2004.00452.pdf\n",
+        "- Repo: https://github.com/facebookresearch/pifuhd\n",
+        "- Project Page: https://shunsukesaito.github.io/PIFuHD/\n",
+        "- 1-minute/5-minute Presentation (see below)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "5DDpqpf2BABR",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 931
+        },
+        "outputId": "55b6f19a-7860-4750-9695-27032ce638e2"
+      },
+      "source": [
+        "import IPython\n",
+        "IPython.display.HTML('<h2>1-Minute Presentation</h2><iframe width=\"720\" height=\"405\" src=\"https://www.youtube.com/embed/-1XYTmm8HhE\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe><br><h2>5-Minute Presentation</h2><iframe width=\"720\" height=\"405\" src=\"https://www.youtube.com/embed/uEDqCxvF5yc\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>')"
+      ],
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "<h2>1-Minute Presentation</h2><iframe width=\"720\" height=\"405\" src=\"https://www.youtube.com/embed/-1XYTmm8HhE\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe><br><h2>5-Minute Presentation</h2><iframe width=\"720\" height=\"405\" src=\"https://www.youtube.com/embed/uEDqCxvF5yc\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 1
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8vZaAyhUJ9QC"
+      },
+      "source": [
+        "## Requirements\n",
+        "- Python 3\n",
+        "- PyTorch tested on 1.4.0\n",
+        "- json\n",
+        "- PIL\n",
+        "- skimage\n",
+        "- tqdm\n",
+        "- numpy\n",
+        "- cv2"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sfPDep8LlP_I"
+      },
+      "source": [
+        "## Help! I'm new to Google Colab\n",
+        "\n",
+        "You can check out the following youtube video on how to upload your own picture and run PIFuHD. **Note that with new update, you can upload your own picture more easily with GUI down below.**\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "zaMP1EitljaA",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 461
+        },
+        "outputId": "6f2b9df4-fdda-49a3-9db8-72a63e232c77"
+      },
+      "source": [
+        "import IPython\n",
+        "IPython.display.HTML('<iframe width=\"720\" height=\"405\" src=\"https://www.youtube.com/embed/LWDGR5v3-3o\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>')"
+      ],
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/IPython/core/display.py:724: UserWarning: Consider using IPython.display.IFrame instead\n",
+            "  warnings.warn(\"Consider using IPython.display.IFrame instead\")\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "<iframe width=\"720\" height=\"405\" src=\"https://www.youtube.com/embed/LWDGR5v3-3o\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 2
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WYhlsDkg1Hwb"
+      },
+      "source": [
+        "## Clone PIFuHD repository"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "BmpEwdOd1G1z",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "2d1252d1-bf47-4fb7-cc75-bdb466650204"
+      },
+      "source": [
+        "!git clone https://github.com/facebookresearch/pifuhd"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Cloning into 'pifuhd'...\n",
+            "remote: Enumerating objects: 222, done.\u001b[K\n",
+            "remote: Counting objects: 100% (126/126), done.\u001b[K\n",
+            "remote: Compressing objects: 100% (44/44), done.\u001b[K\n",
+            "remote: Total 222 (delta 92), reused 82 (delta 82), pack-reused 96 (from 1)\u001b[K\n",
+            "Receiving objects: 100% (222/222), 399.35 KiB | 15.36 MiB/s, done.\n",
+            "Resolving deltas: 100% (114/114), done.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QvQm-A8ESKb2"
+      },
+      "source": [
+        "## Configure input data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "xvle9T10fB6g",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a3ac072e-9669-4a61-b185-3fa9fe9168dc"
+      },
+      "source": [
+        "cd /content/pifuhd/sample_images"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "/content/pifuhd/sample_images\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9SI7Ye1JfIim"
+      },
+      "source": [
+        "**If you want to upload your own picture, run the next cell**. Otherwise, go to the next next cell. Currently PNG, JPEG files are supported."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "jaV_7Yi8fM-B"
+      },
+      "source": [
+        "# from google.colab import files\n",
+        "\n",
+        "# filename = list(files.upload().keys())[0]"
+      ],
+      "execution_count": 5,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "AEzmmB01SOZp"
+      },
+      "source": [
+        "import os\n",
+        "\n",
+        "try:\n",
+        "  image_path = '/content/pifuhd/sample_images/%s' % filename\n",
+        "except:\n",
+        "  image_path = '/content/pifuhd/sample_images/test.png' # example image\n",
+        "image_dir = os.path.dirname(image_path)\n",
+        "file_name = os.path.splitext(os.path.basename(image_path))[0]\n",
+        "\n",
+        "# output pathes\n",
+        "obj_path = '/content/pifuhd/results/pifuhd_final/recon/result_%s_256.obj' % file_name\n",
+        "out_img_path = '/content/pifuhd/results/pifuhd_final/recon/result_%s_256.png' % file_name\n",
+        "video_path = '/content/pifuhd/results/pifuhd_final/recon/result_%s_256.mp4' % file_name\n",
+        "video_display_path = '/content/pifuhd/results/pifuhd_final/result_%s_256_display.mp4' % file_name"
+      ],
+      "execution_count": 6,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "896EC7iQfXkj",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "723eaa7f-a793-479f-f261-c1b07ce79f26"
+      },
+      "source": [
+        "cd /content"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "/content\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JbVmda9J5TDL"
+      },
+      "source": [
+        "## Preprocess (for cropping image)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "UtMjWGNU5STe",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "9eae1a92-7a4d-4da8-cc99-6722406da747"
+      },
+      "source": [
+        "!git clone https://github.com/Daniil-Osokin/lightweight-human-pose-estimation.pytorch.git"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Cloning into 'lightweight-human-pose-estimation.pytorch'...\n",
+            "remote: Enumerating objects: 124, done.\u001b[K\n",
+            "remote: Counting objects: 100% (34/34), done.\u001b[K\n",
+            "remote: Compressing objects: 100% (16/16), done.\u001b[K\n",
+            "remote: Total 124 (delta 21), reused 19 (delta 18), pack-reused 90 (from 1)\u001b[K\n",
+            "Receiving objects: 100% (124/124), 230.77 KiB | 10.99 MiB/s, done.\n",
+            "Resolving deltas: 100% (53/53), done.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "F-vYklhI5dab",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "36994038-c0fa-4f3f-ce38-8cf0e2dec82b"
+      },
+      "source": [
+        "cd /content/lightweight-human-pose-estimation.pytorch/"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "/content/lightweight-human-pose-estimation.pytorch\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "dRod9SOu77I6",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "d110f5ee-2737-4aa4-f80c-82b914466bb0"
+      },
+      "source": [
+        "!wget https://download.01.org/opencv/openvino_training_extensions/models/human_pose_estimation/checkpoint_iter_370000.pth"
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "--2024-08-19 14:10:40--  https://download.01.org/opencv/openvino_training_extensions/models/human_pose_estimation/checkpoint_iter_370000.pth\n",
+            "Resolving download.01.org (download.01.org)... 104.89.123.208, 2600:1417:3f:793::a87, 2600:1417:3f:78d::a87\n",
+            "Connecting to download.01.org (download.01.org)|104.89.123.208|:443... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 87959810 (84M) [application/octet-stream]\n",
+            "Saving to: ‘checkpoint_iter_370000.pth’\n",
+            "\n",
+            "checkpoint_iter_370 100%[===================>]  83.88M   180MB/s    in 0.5s    \n",
+            "\n",
+            "2024-08-19 14:10:41 (180 MB/s) - ‘checkpoint_iter_370000.pth’ saved [87959810/87959810]\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install pycocotools"
+      ],
+      "metadata": {
+        "id": "5CcsRMFAARg0",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "9a97e8bc-3d8b-493c-f3a4-1a5e5513e20b"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: pycocotools in /usr/local/lib/python3.10/dist-packages (2.0.8)\n",
+            "Requirement already satisfied: matplotlib>=2.1.0 in /usr/local/lib/python3.10/dist-packages (from pycocotools) (3.7.1)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from pycocotools) (1.26.4)\n",
+            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.1.0->pycocotools) (1.2.1)\n",
+            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.1.0->pycocotools) (0.12.1)\n",
+            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.1.0->pycocotools) (4.53.1)\n",
+            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.1.0->pycocotools) (1.4.5)\n",
+            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.1.0->pycocotools) (24.1)\n",
+            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.1.0->pycocotools) (9.4.0)\n",
+            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.1.0->pycocotools) (3.1.2)\n",
+            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.1.0->pycocotools) (2.8.2)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=2.1.0->pycocotools) (1.16.0)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# !pip uninstall numpy\n",
+        "# !pip install numpy==1.20.0"
+      ],
+      "metadata": {
+        "id": "jTHmiIYQumJq"
+      },
+      "execution_count": 12,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "PdRcDXe38lHB"
+      },
+      "source": [
+        "import torch\n",
+        "import cv2\n",
+        "import numpy as np\n",
+        "from models.with_mobilenet import PoseEstimationWithMobileNet\n",
+        "from modules.keypoints import extract_keypoints, group_keypoints\n",
+        "from modules.load_state import load_state\n",
+        "from modules.pose import Pose, track_poses\n",
+        "import demo\n",
+        "\n",
+        "def get_rect(net, images, height_size):\n",
+        "    net = net.eval()\n",
+        "\n",
+        "    stride = 8\n",
+        "    upsample_ratio = 4\n",
+        "    num_keypoints = Pose.num_kpts\n",
+        "    previous_poses = []\n",
+        "    delay = 33\n",
+        "    for image in images:\n",
+        "        rect_path = image.replace('.%s' % (image.split('.')[-1]), '_rect.txt')\n",
+        "        img = cv2.imread(image, cv2.IMREAD_COLOR)\n",
+        "        orig_img = img.copy()\n",
+        "        orig_img = img.copy()\n",
+        "        heatmaps, pafs, scale, pad = demo.infer_fast(net, img, height_size, stride, upsample_ratio, cpu=False)\n",
+        "\n",
+        "        total_keypoints_num = 0\n",
+        "        all_keypoints_by_type = []\n",
+        "        for kpt_idx in range(num_keypoints):  # 19th for bg\n",
+        "            total_keypoints_num += extract_keypoints(heatmaps[:, :, kpt_idx], all_keypoints_by_type, total_keypoints_num)\n",
+        "\n",
+        "        pose_entries, all_keypoints = group_keypoints(all_keypoints_by_type, pafs)\n",
+        "        for kpt_id in range(all_keypoints.shape[0]):\n",
+        "            all_keypoints[kpt_id, 0] = (all_keypoints[kpt_id, 0] * stride / upsample_ratio - pad[1]) / scale\n",
+        "            all_keypoints[kpt_id, 1] = (all_keypoints[kpt_id, 1] * stride / upsample_ratio - pad[0]) / scale\n",
+        "        current_poses = []\n",
+        "\n",
+        "        rects = []\n",
+        "        for n in range(len(pose_entries)):\n",
+        "            if len(pose_entries[n]) == 0:\n",
+        "                continue\n",
+        "            pose_keypoints = np.ones((num_keypoints, 2), dtype=int) * -1\n",
+        "            valid_keypoints = []\n",
+        "            for kpt_id in range(num_keypoints):\n",
+        "                if pose_entries[n][kpt_id] != -1.0:  # keypoint was found\n",
+        "                    pose_keypoints[kpt_id, 0] = int(all_keypoints[int(pose_entries[n][kpt_id]), 0])\n",
+        "                    pose_keypoints[kpt_id, 1] = int(all_keypoints[int(pose_entries[n][kpt_id]), 1])\n",
+        "                    valid_keypoints.append([pose_keypoints[kpt_id, 0], pose_keypoints[kpt_id, 1]])\n",
+        "            valid_keypoints = np.array(valid_keypoints)\n",
+        "\n",
+        "            if pose_entries[n][10] != -1.0 or pose_entries[n][13] != -1.0:\n",
+        "              pmin = valid_keypoints.min(0)\n",
+        "              pmax = valid_keypoints.max(0)\n",
+        "\n",
+        "              center = (0.5 * (pmax[:2] + pmin[:2])).astype(int)\n",
+        "              radius = int(0.65 * max(pmax[0]-pmin[0], pmax[1]-pmin[1]))\n",
+        "            elif pose_entries[n][10] == -1.0 and pose_entries[n][13] == -1.0 and pose_entries[n][8] != -1.0 and pose_entries[n][11] != -1.0:\n",
+        "              # if leg is missing, use pelvis to get cropping\n",
+        "              center = (0.5 * (pose_keypoints[8] + pose_keypoints[11])).astype(int)\n",
+        "              radius = int(1.45*np.sqrt(((center[None,:] - valid_keypoints)**2).sum(1)).max(0))\n",
+        "              center[1] += int(0.05*radius)\n",
+        "            else:\n",
+        "              center = np.array([img.shape[1]//2,img.shape[0]//2])\n",
+        "              radius = max(img.shape[1]//2,img.shape[0]//2)\n",
+        "\n",
+        "            x1 = center[0] - radius\n",
+        "            y1 = center[1] - radius\n",
+        "\n",
+        "            rects.append([x1, y1, 2*radius, 2*radius])\n",
+        "\n",
+        "        np.savetxt(rect_path, np.array(rects), fmt='%d')"
+      ],
+      "execution_count": 13,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "M6cGZD6f6IaY"
+      },
+      "source": [
+        "net = PoseEstimationWithMobileNet()\n",
+        "checkpoint = torch.load('checkpoint_iter_370000.pth', map_location='cpu')\n",
+        "load_state(net, checkpoint)\n",
+        "\n",
+        "get_rect(net.cuda(), [image_path], 512)"
+      ],
+      "execution_count": 14,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Y0rgMInwTt0s"
+      },
+      "source": [
+        "## Download the Pretrained Model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "UrIcZweSNRFI",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "e77ebb3a-80e8-48bf-ece4-ba24c1f53c4c"
+      },
+      "source": [
+        "cd /content/pifuhd/"
+      ],
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "/content/pifuhd\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "k3jjm6HuQRk8",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "8a5a71c0-8c40-4768-b032-862978b0ccdc"
+      },
+      "source": [
+        "!sh ./scripts/download_trained_model.sh"
+      ],
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "+ mkdir -p checkpoints\n",
+            "+ cd checkpoints\n",
+            "+ wget https://dl.fbaipublicfiles.com/pifuhd/checkpoints/pifuhd.pt pifuhd.pt\n",
+            "--2024-08-19 14:10:54--  https://dl.fbaipublicfiles.com/pifuhd/checkpoints/pifuhd.pt\n",
+            "Resolving dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)... 13.35.18.103, 13.35.18.15, 13.35.18.87, ...\n",
+            "Connecting to dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)|13.35.18.103|:443... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 1548375177 (1.4G) [application/octet-stream]\n",
+            "Saving to: ‘pifuhd.pt’\n",
+            "\n",
+            "pifuhd.pt           100%[===================>]   1.44G   159MB/s    in 14s     \n",
+            "\n",
+            "2024-08-19 14:11:07 (108 MB/s) - ‘pifuhd.pt’ saved [1548375177/1548375177]\n",
+            "\n",
+            "--2024-08-19 14:11:07--  http://pifuhd.pt/\n",
+            "Resolving pifuhd.pt (pifuhd.pt)... failed: Name or service not known.\n",
+            "wget: unable to resolve host address ‘pifuhd.pt’\n",
+            "FINISHED --2024-08-19 14:11:07--\n",
+            "Total wall clock time: 14s\n",
+            "Downloaded: 1 files, 1.4G in 14s (108 MB/s)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6heKcA-0QEBw"
+      },
+      "source": [
+        "## Run PIFuHD!\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy"
+      ],
+      "metadata": {
+        "id": "JECF8rO-ySkT"
+      },
+      "execution_count": 17,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "numpy.version.version"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 36
+        },
+        "id": "FsyHgfSoyjxj",
+        "outputId": "ce315da9-115a-46fa-9399-f245738bc6b9"
+      },
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'1.26.4'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 18
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "5995t2PnQTmG",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "aef871ca-9a8f-453c-9f44-02069301b5cd"
+      },
+      "source": [
+        "# Warning: all images with the corresponding rectangle files under -i will be processed.\n",
+        "!python -m apps.simple_test -r 256 --use_rect -i $image_dir\n",
+        "\n",
+        "# seems that 256 is the maximum resolution that can fit into Google Colab.\n",
+        "# If you want to reconstruct a higher-resolution mesh, please try with your own machine."
+      ],
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Resuming from  ./checkpoints/pifuhd.pt\n",
+            "Warning: opt is overwritten.\n",
+            "test data size:  1\n",
+            "initialize network with normal\n",
+            "initialize network with normal\n",
+            "generate mesh (test) ...\n",
+            "  0% 0/1 [00:00<?, ?it/s]./results/pifuhd_final/recon/result_test_256.obj\n",
+            "/content/pifuhd/lib/sdf.py:93: FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar.\n",
+            "  notprocessed = np.zeros(resolution, dtype=np.bool)\n",
+            "module 'numpy' has no attribute 'bool'.\n",
+            "`np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
+            "The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:\n",
+            "    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+            "100% 1/1 [00:02<00:00,  2.39s/it]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EUZ8Nt5rNFXZ"
+      },
+      "source": [
+        "## Render the result"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "5xp5s5uiOiDv",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "8583c884-a1dd-4768-ca79-166f91f83519"
+      },
+      "source": [
+        "import sys\n",
+        "import torch\n",
+        "pyt_version_str=torch.__version__.split(\"+\")[0].replace(\".\", \"\")\n",
+        "version_str=\"\".join([\n",
+        "    f\"py3{sys.version_info.minor}_cu\",\n",
+        "    torch.version.cuda.replace(\".\",\"\"),\n",
+        "    f\"_pyt{pyt_version_str}\"\n",
+        "])\n",
+        "!pip install fvcore iopath\n",
+        "!pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html"
+      ],
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting fvcore\n",
+            "  Downloading fvcore-0.1.5.post20221221.tar.gz (50 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.2/50.2 kB\u001b[0m \u001b[31m4.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "Collecting iopath\n",
+            "  Downloading iopath-0.1.10.tar.gz (42 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.2/42.2 kB\u001b[0m \u001b[31m3.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from fvcore) (1.26.4)\n",
+            "Collecting yacs>=0.1.6 (from fvcore)\n",
+            "  Downloading yacs-0.1.8-py3-none-any.whl.metadata (639 bytes)\n",
+            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from fvcore) (6.0.2)\n",
+            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from fvcore) (4.66.5)\n",
+            "Requirement already satisfied: termcolor>=1.1 in /usr/local/lib/python3.10/dist-packages (from fvcore) (2.4.0)\n",
+            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from fvcore) (9.4.0)\n",
+            "Requirement already satisfied: tabulate in /usr/local/lib/python3.10/dist-packages (from fvcore) (0.9.0)\n",
+            "Requirement already satisfied: typing_extensions in /usr/local/lib/python3.10/dist-packages (from iopath) (4.12.2)\n",
+            "Collecting portalocker (from iopath)\n",
+            "  Downloading portalocker-2.10.1-py3-none-any.whl.metadata (8.5 kB)\n",
+            "Downloading yacs-0.1.8-py3-none-any.whl (14 kB)\n",
+            "Downloading portalocker-2.10.1-py3-none-any.whl (18 kB)\n",
+            "Building wheels for collected packages: fvcore, iopath\n",
+            "  Building wheel for fvcore (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for fvcore: filename=fvcore-0.1.5.post20221221-py3-none-any.whl size=61395 sha256=261e403ecfe298c1f5ee9d92c857ad05ad1da7ffdd20847907dbd243b52f9edc\n",
+            "  Stored in directory: /root/.cache/pip/wheels/01/c0/af/77c1cf53a1be9e42a52b48e5af2169d40ec2e89f7362489dd0\n",
+            "  Building wheel for iopath (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for iopath: filename=iopath-0.1.10-py3-none-any.whl size=31529 sha256=17ecad6f244d26b7609e55888c220521833c490e67a1bfe9405f20b47f9ff1ff\n",
+            "  Stored in directory: /root/.cache/pip/wheels/9a/a3/b6/ac0fcd1b4ed5cfeb3db92e6a0e476cfd48ed0df92b91080c1d\n",
+            "Successfully built fvcore iopath\n",
+            "Installing collected packages: yacs, portalocker, iopath, fvcore\n",
+            "Successfully installed fvcore-0.1.5.post20221221 iopath-0.1.10 portalocker-2.10.1 yacs-0.1.8\n",
+            "Looking in links: https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/py310_cu121_pyt231/download.html\n",
+            "Collecting pytorch3d\n",
+            "  Downloading https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/py310_cu121_pyt231/pytorch3d-0.7.6-cp310-cp310-linux_x86_64.whl (20.5 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m20.5/20.5 MB\u001b[0m \u001b[31m146.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: fvcore in /usr/local/lib/python3.10/dist-packages (from pytorch3d) (0.1.5.post20221221)\n",
+            "Requirement already satisfied: iopath in /usr/local/lib/python3.10/dist-packages (from pytorch3d) (0.1.10)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from fvcore->pytorch3d) (1.26.4)\n",
+            "Requirement already satisfied: yacs>=0.1.6 in /usr/local/lib/python3.10/dist-packages (from fvcore->pytorch3d) (0.1.8)\n",
+            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from fvcore->pytorch3d) (6.0.2)\n",
+            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from fvcore->pytorch3d) (4.66.5)\n",
+            "Requirement already satisfied: termcolor>=1.1 in /usr/local/lib/python3.10/dist-packages (from fvcore->pytorch3d) (2.4.0)\n",
+            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from fvcore->pytorch3d) (9.4.0)\n",
+            "Requirement already satisfied: tabulate in /usr/local/lib/python3.10/dist-packages (from fvcore->pytorch3d) (0.9.0)\n",
+            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from iopath->pytorch3d) (4.12.2)\n",
+            "Requirement already satisfied: portalocker in /usr/local/lib/python3.10/dist-packages (from iopath->pytorch3d) (2.10.1)\n",
+            "Installing collected packages: pytorch3d\n",
+            "Successfully installed pytorch3d-0.7.6\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from lib.colab_util import generate_video_from_obj, set_renderer, video\n",
+        "\n",
+        "renderer = set_renderer()"
+      ],
+      "metadata": {
+        "id": "9bMFZ9bDz5rh"
+      },
+      "execution_count": 21,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "afwL_-ROCmDf",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 356
+        },
+        "outputId": "06a5eecd-1032-4844-a29b-00990fe638a5"
+      },
+      "source": [
+        "\n",
+        "generate_video_from_obj(obj_path, out_img_path, video_path, renderer)\n",
+        "\n",
+        "# we cannot play a mp4 video generated by cv2\n",
+        "!ffmpeg -i $video_path -vcodec libx264 $video_display_path -y -loglevel quiet\n",
+        "video(video_display_path)"
+      ],
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "FileNotFoundError",
+          "evalue": "[Errno 2] No such file or directory: '/content/pifuhd/results/pifuhd_final/recon/result_test_256.obj'",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-22-a58c80d9ae68>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgenerate_video_from_obj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout_img_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvideo_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# we cannot play a mp4 video generated by cv2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msystem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'ffmpeg -i $video_path -vcodec libx264 $video_display_path -y -loglevel quiet'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mvideo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvideo_display_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/content/pifuhd/lib/colab_util.py\u001b[0m in \u001b[0;36mgenerate_video_from_obj\u001b[0;34m(obj_path, image_path, video_path, renderer)\u001b[0m\n\u001b[1;32m    107\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m     \u001b[0;31m# Load obj file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 109\u001b[0;31m     \u001b[0mverts_rgb_colors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_verts_rgb_colors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    110\u001b[0m     \u001b[0mverts_rgb_colors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mverts_rgb_colors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    111\u001b[0m     \u001b[0mtextures\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTexturesVertex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mverts_features\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverts_rgb_colors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/content/pifuhd/lib/colab_util.py\u001b[0m in \u001b[0;36mget_verts_rgb_colors\u001b[0;34m(obj_path)\u001b[0m\n\u001b[1;32m     88\u001b[0m   \u001b[0mrgb_colors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 90\u001b[0;31m   \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     91\u001b[0m   \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadlines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     92\u001b[0m   \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/content/pifuhd/results/pifuhd_final/recon/result_test_256.obj'"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eUEXAvcvkVYV"
+      },
+      "source": [
+        "## Tips for Inputs: My results are broken!\n",
+        "\n",
+        "(Kudos to those who share results on twitter with [#pifuhd](https://twitter.com/search?q=%23pifuhd&src=recent_search_click&f=live) tag!!!!)\n",
+        "\n",
+        "Due to the limited variation in the training data, your results might be broken sometimes. Here I share some useful tips to get resonable results.\n",
+        "\n",
+        "*   Use high-res image. The model is trained with 1024x1024 images. Use at least 512x512 with fine-details. Low-res images and JPEG artifacts may result in unsatisfactory results.\n",
+        "*   Use an image with a single person. If the image contain multiple people, reconstruction quality is likely degraded.\n",
+        "*   Front facing with standing works best (or with fashion pose)\n",
+        "*   The entire body is covered within the image. (Note: now missing legs is partially supported)\n",
+        "*   Make sure the input image is well lit. Exteremy dark or bright image and strong shadow often create artifacts.\n",
+        "*   I recommend nearly parallel camera angle to the ground. High camera height may result in distorted legs or high heels.\n",
+        "*   If the background is cluttered, use less complex background or try removing it using https://www.remove.bg/ before processing.\n",
+        "*   It's trained with human only. Anime characters may not work well (To my surprise, indeed many people tried it!!).\n",
+        "*   Search on twitter with [#pifuhd](https://twitter.com/search?q=%23pifuhd&src=recent_search_click&f=live) tag to get a better sense of what succeeds and what fails.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "u6U0K5CNAO_u"
+      },
+      "source": [
+        "## Share your result!\n",
+        "Please share your results with[ #pifuhd](https://twitter.com/search?q=%23pifuhd&src=recent_search_click&f=live) tag on Twitter. Sharing your good/bad results helps and encourages the authors to further push towards producition-quality human digitization at home.\n",
+        "**As the tweet buttom below doesn't add the result video automatically, please download the result video above and manually add it to the tweet.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "1CBxbdrM9F-9"
+      },
+      "source": [
+        "import IPython\n",
+        "IPython.display.HTML('<a href=\"https://twitter.com/intent/tweet?button_hashtag=pifuhd&ref_src=twsrc%5Etfw\" class=\"twitter-hashtag-button\" data-size=\"large\" data-text=\"Google Colab Link: \" data-url=\"https://bit.ly/37sfogZ\" data-show-count=\"false\">Tweet #pifuhd</a><script async src=\"https://platform.twitter.com/widgets.js\" charset=\"utf-8\"></script>  (Don\\'t forget to add your result to the tweet!)')"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2d-1pR8UR7PR"
+      },
+      "source": [
+        "## Cool Applications\n",
+        "Special thanks to those who play with PIFuHD and came up with many creative applications!! If you made any cool applications, please tweet your demo with [#pifuhd](https://twitter.com/search?q=%23pifuhd&src=recent_search_click&f=live). I'm constantly checking results there.\n",
+        "If you need complete texture on the mesh, please try my previous work [PIFu](https://github.com/shunsukesaito/PIFu) as well! It supports 3D reconstruction + texturing from a single image although the geometry quality may not be as good as PIFuHD."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "68JDAYJFSFMV"
+      },
+      "source": [
+        "IPython.display.HTML('<h2>Rigging (Mixamo) + Photoreal Rendering (Blender)</h2><blockquote class=\"twitter-tweet\"><p lang=\"pt\" dir=\"ltr\">vcs ainda tem a PACHORRA de me dizer que eu não sei dançar<a href=\"https://twitter.com/hashtag/b3d?src=hash&amp;ref_src=twsrc%5Etfw\">#b3d</a> <a href=\"https://twitter.com/hashtag/pifuhd?src=hash&amp;ref_src=twsrc%5Etfw\">#pifuhd</a> <a href=\"https://t.co/kHCnLh6zxH\">pic.twitter.com/kHCnLh6zxH</a></p>&mdash; lukas arendero (@lukazvd) <a href=\"https://twitter.com/lukazvd/status/1274810484798128131?ref_src=twsrc%5Etfw\">June 21, 2020</a></blockquote> <script async src=\"https://platform.twitter.com/widgets.js\" charset=\"utf-8\"></script><h2>FaceApp + Rigging (Mixamo)</h2><blockquote class=\"twitter-tweet\"><p lang=\"ja\" dir=\"ltr\">カツラかぶってる自分に見える <a href=\"https://twitter.com/hashtag/pifuhd?src=hash&amp;ref_src=twsrc%5Etfw\">#pifuhd</a> <a href=\"https://t.co/V8o7VduTiG\">pic.twitter.com/V8o7VduTiG</a></p>&mdash; Shuhei Tsuchida (@shuhei2306) <a href=\"https://twitter.com/shuhei2306/status/1274507242910314498?ref_src=twsrc%5Etfw\">June 21, 2020</a></blockquote> <script async src=\"https://platform.twitter.com/widgets.js\" charset=\"utf-8\"></script><h2>Rigging (Mixamo) + AR (Adobe Aero)</AR><blockquote class=\"twitter-tweet\"><p lang=\"ja\" dir=\"ltr\">写真→PIFuHD→Mixamo→AdobeAeroでサウンド付きARを作成。Zip化してLINEでARコンテンツを共有。<br>写真が1枚あれば簡単にARの3Dアニメーションが作れる時代…凄い。<a href=\"https://twitter.com/hashtag/PIFuHD?src=hash&amp;ref_src=twsrc%5Etfw\">#PIFuHD</a> <a href=\"https://twitter.com/hashtag/AdobeAero?src=hash&amp;ref_src=twsrc%5Etfw\">#AdobeAero</a> <a href=\"https://twitter.com/hashtag/Mixamo?src=hash&amp;ref_src=twsrc%5Etfw\">#Mixamo</a> <a href=\"https://t.co/CbiMi4gZ0K\">pic.twitter.com/CbiMi4gZ0K</a></p>&mdash; モジョン (@mojon1) <a href=\"https://twitter.com/mojon1/status/1273217947872317441?ref_src=twsrc%5Etfw\">June 17, 2020</a></blockquote> <script async src=\"https://platform.twitter.com/widgets.js\" charset=\"utf-8\"></script><h2>3D Printing</h2><blockquote class=\"twitter-tweet\"><p lang=\"ja\" dir=\"ltr\"><a href=\"https://twitter.com/hashtag/pifuhd?src=hash&amp;ref_src=twsrc%5Etfw\">#pifuhd</a> 楽しい〜<br>小さい自分プリントした <a href=\"https://t.co/4qyWuij0Hs\">pic.twitter.com/4qyWuij0Hs</a></p>&mdash; isb (@vxzxzxzxv) <a href=\"https://twitter.com/vxzxzxzxv/status/1273136266406694913?ref_src=twsrc%5Etfw\">June 17, 2020</a></blockquote> <script async src=\"https://platform.twitter.com/widgets.js\" charset=\"utf-8\"></script>')"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "lX5CTTW_KWhQ"
+      },
+      "source": [],
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file