├── .gitignore ├── .gitmodules ├── 3D Avatar Pipeline.ipynb ├── 3D_Avatar_Pipeline.py ├── LICENSE ├── README.md ├── Results └── .gitkeep ├── Rtree-0.9.7-cp37-cp37m-win_amd64.whl ├── binvox ├── binvox.exe ├── input ├── .gitkeep └── test.png ├── mixamo └── animations │ └── Idle.dae ├── preprocess_img_pose.py ├── requirements.txt ├── rignet_script.py └── screenshots └── .gitkeep /.gitignore: -------------------------------------------------------------------------------- 1 | ./Results/* 2 | ./Screenshots/* 3 | ./input/* 4 | ./Checkpoints/* 5 | 6 | !./Results/.gitkeep 7 | !./Screenshots/.gitkeep 8 | !./input/.gitkeep 9 | -------------------------------------------------------------------------------- /.gitmodules: -------------------------------------------------------------------------------- 1 | [submodule "pifuhd"] 2 | path = pifuhd 3 | url = https://github.com/facebookresearch/pifuhd 4 | [submodule "RigNet"] 5 | path = RigNet 6 | url = https://github.com/zhan-xu/RigNet 7 | [submodule "lightweight-human-pose-estimation.pytorch"] 8 | path = lightweight-human-pose-estimation.pytorch 9 | url = https://github.com/Daniil-Osokin/lightweight-human-pose-estimation.pytorch 10 | -------------------------------------------------------------------------------- /3D Avatar Pipeline.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "id": "f166fde2", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "#Running PIFuHD - https://shunsukesaito.github.io/PIFuHD/\n", 11 | "'''\n", 12 | "To make it cross compatible with RigNet - https://zhan-xu.github.io/rig-net/\n", 13 | "\n", 14 | " conda create -n python=3.7\n", 15 | " conda activate \n", 16 | " conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.1 -c pytorch\n", 17 | " pip install numpy scipy matplotlib tensorboard open3d==0.9.0 opencv-python\n", 18 | "\n", 19 | "Requirements - pip install -r requirements.txt\n", 20 | "\n", 21 | " Python 3\n", 22 | " PyTorch tested on 1.4.0\n", 23 | " json\n", 24 | " PIL\n", 25 | " skimage\n", 26 | " tqdm\n", 27 | " numpy\n", 28 | " cv2\n", 29 | "'''" 30 | ] 31 | }, 32 | { 33 | "cell_type": "code", 34 | "execution_count": null, 35 | "id": "1252c062", 36 | "metadata": {}, 37 | "outputs": [], 38 | "source": [ 39 | "ls" 40 | ] 41 | }, 42 | { 43 | "cell_type": "code", 44 | "execution_count": null, 45 | "id": "c39734ff", 46 | "metadata": {}, 47 | "outputs": [], 48 | "source": [ 49 | "cd pifuHD\\pifuhd\\sample_images" 50 | ] 51 | }, 52 | { 53 | "cell_type": "code", 54 | "execution_count": null, 55 | "id": "4cde3412", 56 | "metadata": {}, 57 | "outputs": [], 58 | "source": [ 59 | "%cd ..\n", 60 | "%cd lightweight-human-pose-estimation.pytorch" 61 | ] 62 | }, 63 | { 64 | "cell_type": "code", 65 | "execution_count": null, 66 | "id": "92811538", 67 | "metadata": {}, 68 | "outputs": [], 69 | "source": [ 70 | "!pip install -U scikit-image\n", 71 | "!pip install -U cython \n", 72 | "!pip install git+https://github.com/philferriere/cocoapi.git#egg=pycocotools^&subdirectory=PythonAPI" 73 | ] 74 | }, 75 | { 76 | "cell_type": "code", 77 | "execution_count": 2, 78 | "id": "ec3a1af8", 79 | "metadata": {}, 80 | "outputs": [ 81 | { 82 | "data": { 83 | "text/plain": [ 84 | "'D:\\\\CG_Source\\\\NeRFs\\\\pifuHD\\\\pifuhd'" 85 | ] 86 | }, 87 | "execution_count": 2, 88 | "metadata": {}, 89 | "output_type": "execute_result" 90 | } 91 | ], 92 | "source": [ 93 | "pwd" 94 | ] 95 | }, 96 | { 97 | "cell_type": "code", 98 | "execution_count": null, 99 | "id": "3c19c8da", 100 | "metadata": {}, 101 | "outputs": [], 102 | "source": [ 103 | "import torch\n", 104 | "import cv2\n", 105 | "import numpy as np\n", 106 | "from models.with_mobilenet import PoseEstimationWithMobileNet\n", 107 | "from modules.keypoints import extract_keypoints, group_keypoints\n", 108 | "from modules.load_state import load_state\n", 109 | "from modules.pose import Pose, track_poses\n", 110 | "import demo\n", 111 | "\n", 112 | "def get_rect(net, images, height_size):\n", 113 | " net = net.eval()\n", 114 | "\n", 115 | " stride = 8\n", 116 | " upsample_ratio = 4\n", 117 | " num_keypoints = Pose.num_kpts\n", 118 | " previous_poses = []\n", 119 | " delay = 33\n", 120 | " for image in images:\n", 121 | " rect_path = image.replace('.%s' % (image.split('.')[-1]), '_rect.txt')\n", 122 | " img = cv2.imread(image, cv2.IMREAD_COLOR)\n", 123 | " orig_img = img.copy()\n", 124 | " orig_img = img.copy()\n", 125 | " heatmaps, pafs, scale, pad = demo.infer_fast(net, img, height_size, stride, upsample_ratio, cpu=False)\n", 126 | "\n", 127 | " total_keypoints_num = 0\n", 128 | " all_keypoints_by_type = []\n", 129 | " for kpt_idx in range(num_keypoints): # 19th for bg\n", 130 | " total_keypoints_num += extract_keypoints(heatmaps[:, :, kpt_idx], all_keypoints_by_type, total_keypoints_num)\n", 131 | "\n", 132 | " pose_entries, all_keypoints = group_keypoints(all_keypoints_by_type, pafs)\n", 133 | " for kpt_id in range(all_keypoints.shape[0]):\n", 134 | " all_keypoints[kpt_id, 0] = (all_keypoints[kpt_id, 0] * stride / upsample_ratio - pad[1]) / scale\n", 135 | " all_keypoints[kpt_id, 1] = (all_keypoints[kpt_id, 1] * stride / upsample_ratio - pad[0]) / scale\n", 136 | " current_poses = []\n", 137 | "\n", 138 | " rects = []\n", 139 | " for n in range(len(pose_entries)):\n", 140 | " if len(pose_entries[n]) == 0:\n", 141 | " continue\n", 142 | " pose_keypoints = np.ones((num_keypoints, 2), dtype=np.int32) * -1\n", 143 | " valid_keypoints = []\n", 144 | " for kpt_id in range(num_keypoints):\n", 145 | " if pose_entries[n][kpt_id] != -1.0: # keypoint was found\n", 146 | " pose_keypoints[kpt_id, 0] = int(all_keypoints[int(pose_entries[n][kpt_id]), 0])\n", 147 | " pose_keypoints[kpt_id, 1] = int(all_keypoints[int(pose_entries[n][kpt_id]), 1])\n", 148 | " valid_keypoints.append([pose_keypoints[kpt_id, 0], pose_keypoints[kpt_id, 1]])\n", 149 | " valid_keypoints = np.array(valid_keypoints)\n", 150 | " \n", 151 | " if pose_entries[n][10] != -1.0 or pose_entries[n][13] != -1.0:\n", 152 | " pmin = valid_keypoints.min(0)\n", 153 | " pmax = valid_keypoints.max(0)\n", 154 | "\n", 155 | " center = (0.5 * (pmax[:2] + pmin[:2])).astype(np.int)\n", 156 | " radius = int(0.65 * max(pmax[0]-pmin[0], pmax[1]-pmin[1]))\n", 157 | " 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", 158 | " # if leg is missing, use pelvis to get cropping\n", 159 | " center = (0.5 * (pose_keypoints[8] + pose_keypoints[11])).astype(np.int)\n", 160 | " radius = int(1.45*np.sqrt(((center[None,:] - valid_keypoints)**2).sum(1)).max(0))\n", 161 | " center[1] += int(0.05*radius)\n", 162 | " else:\n", 163 | " center = np.array([img.shape[1]//2,img.shape[0]//2])\n", 164 | " radius = max(img.shape[1]//2,img.shape[0]//2)\n", 165 | "\n", 166 | " x1 = center[0] - radius\n", 167 | " y1 = center[1] - radius\n", 168 | "\n", 169 | " rects.append([x1, y1, 2*radius, 2*radius])\n", 170 | "\n", 171 | " np.savetxt(rect_path, np.array(rects), fmt='%d')" 172 | ] 173 | }, 174 | { 175 | "cell_type": "code", 176 | "execution_count": 1, 177 | "id": "18494d1b", 178 | "metadata": {}, 179 | "outputs": [ 180 | { 181 | "name": "stdout", 182 | "output_type": "stream", 183 | "text": [ 184 | "D:\\CG_Source\\NeRFs\\pifuHD\\pifuhd\n" 185 | ] 186 | } 187 | ], 188 | "source": [ 189 | "%cd pifuHD/pifuhd/\n" 190 | ] 191 | }, 192 | { 193 | "cell_type": "code", 194 | "execution_count": 2, 195 | "id": "63a903f0", 196 | "metadata": {}, 197 | "outputs": [ 198 | { 199 | "name": "stdout", 200 | "output_type": "stream", 201 | "text": [ 202 | "Resuming from ./checkpoints/pifuhd.pt\n", 203 | "Warning: opt is overwritten.\n", 204 | "test data size: 1\n", 205 | "initialize network with normal\n" 206 | ] 207 | }, 208 | { 209 | "name": "stderr", 210 | "output_type": "stream", 211 | "text": [ 212 | 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Tried to allocate 256.00 MiB (GPU 0; 6.00 GiB total capacity; 4.05 GiB already allocated; 63.62 MiB free; 4.26 GiB reserved in total by PyTorch)" 250 | ] 251 | } 252 | ], 253 | "source": [ 254 | "# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.\n", 255 | "\n", 256 | "\n", 257 | "from apps.recon import reconWrapper\n", 258 | "import argparse\n", 259 | "\n", 260 | "\n", 261 | "###############################################################################################\n", 262 | "## Setting\n", 263 | "###############################################################################################\n", 264 | "'''parser = argparse.ArgumentParser()\n", 265 | "parser.add_argument('-i', '--input_path', type=str, default='./sample_images')\n", 266 | "parser.add_argument('-o', '--out_path', type=str, default='./results')\n", 267 | "parser.add_argument('-c', '--ckpt_path', type=str, default='./checkpoints/pifuhd.pt')\n", 268 | "parser.add_argument('-r', '--resolution', type=int, default=512)\n", 269 | "parser.add_argument('--use_rect', action='store_true', help='use rectangle for cropping')\n", 270 | "args = parser.parse_args()'''\n", 271 | "###############################################################################################\n", 272 | "## Upper PIFu\n", 273 | "###############################################################################################\n", 274 | "\n", 275 | "#resolution = str(args.resolution)\n", 276 | "\n", 277 | "start_id = -1\n", 278 | "end_id = -1\n", 279 | "cmd = ['--dataroot', './sample_images', '--results_path', './results',\\\n", 280 | " '--loadSize', '1024', '--resolution', str(128), '--load_netMR_checkpoint_path', \\\n", 281 | " './checkpoints/pifuhd.pt',\\\n", 282 | " '--start_id', '%d' % start_id, '--end_id', '%d' % end_id]\n", 283 | "reconWrapper(cmd, 'store_true')" 284 | ] 285 | }, 286 | { 287 | "cell_type": "code", 288 | "execution_count": 3, 289 | "id": "745a2b85", 290 | "metadata": { 291 | "scrolled": false 292 | }, 293 | "outputs": [ 294 | { 295 | "name": "stdout", 296 | "output_type": "stream", 297 | "text": [ 298 | "Resuming from ./checkpoints/pifuhd.pt\n", 299 | "Warning: opt is overwritten.\n", 300 | "test data size: 1\n", 301 | "initialize network with normal\n", 302 | "initialize network with normal\n", 303 | "generate mesh (test) ...\n", 304 | "./results/pifuhd_final/recon/result_test_512.obj" 305 | ] 306 | }, 307 | { 308 | "name": "stderr", 309 | "output_type": "stream", 310 | "text": [ 311 | "\n", 312 | " 0%| | 0/1 [00:00\n", 320 | " reconWrapper(cmd, args.use_rect)\n", 321 | " File \"D:\\CG_Source\\NeRFs\\pifuHD\\pifuhd\\apps\\recon.py\", line 220, in reconWrapper\n", 322 | " recon(opt, use_rect)\n", 323 | " File \"D:\\CG_Source\\NeRFs\\pifuHD\\pifuhd\\apps\\recon.py\", line 210, in recon\n", 324 | " gen_mesh(opt.resolution, netMR, cuda, test_data, save_path, components=opt.use_compose)\n", 325 | " File \"D:\\CG_Source\\NeRFs\\pifuHD\\pifuhd\\apps\\recon.py\", line 39, in gen_mesh\n", 326 | " net.filter_local(image_tensor[:,None])\n", 327 | " File \"D:\\CG_Source\\NeRFs\\pifuHD\\pifuhd\\lib\\model\\HGPIFuMRNet.py\", line 117, in filter_local\n", 328 | " self.im_feat_list, self.normx = self.image_filter(images.view(-1,*images.size()[2:]))\n", 329 | " File \"C:\\miniconda3\\envs\\3d_avatar\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 722, in _call_impl\n", 330 | " result = self.forward(*input, **kwargs)\n", 331 | " File \"D:\\CG_Source\\NeRFs\\pifuHD\\pifuhd\\lib\\model\\HGFilters.py\", line 195, in forward\n", 332 | " hg = self._modules['m' + str(i)](previous)\n", 333 | " File \"C:\\miniconda3\\envs\\3d_avatar\\lib\\site-packages\\torch\\nn\\modules\\module.py\", line 722, in _call_impl\n", 334 | " result = self.forward(*input, **kwargs)\n", 335 | " File \"D:\\CG_Source\\NeRFs\\pifuHD\\pifuhd\\lib\\model\\HGFilters.py\", line 117, in forward\n", 336 | " return self._forward(self.depth, x)\n", 337 | " File \"D:\\CG_Source\\NeRFs\\pifuHD\\pifuhd\\lib\\model\\HGFilters.py\", line 114, in _forward\n", 338 | " return up1 + up2\n", 339 | "RuntimeError: CUDA out of memory. Tried to allocate 256.00 MiB (GPU 0; 6.00 GiB total capacity; 4.05 GiB already allocated; 63.62 MiB free; 4.26 GiB reserved in total by PyTorch)\n" 340 | ] 341 | }, 342 | { 343 | "name": "stdout", 344 | "output_type": "stream", 345 | "text": [ 346 | "\n" 347 | ] 348 | } 349 | ], 350 | "source": [ 351 | "#checkpoints downloaded from - https://dl.fbaipublicfiles.com/pifuhd/checkpoints/pifuhd.pt\n", 352 | "!python -m apps.simple_test" 353 | ] 354 | }, 355 | { 356 | "cell_type": "code", 357 | "execution_count": 4, 358 | "id": "6f1398b8", 359 | "metadata": {}, 360 | "outputs": [ 361 | { 362 | "ename": "NameError", 363 | "evalue": "name 'torch' is not defined", 364 | "output_type": "error", 365 | "traceback": [ 366 | "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", 367 | "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", 368 | "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m#clearing memory in GPU\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mempty_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", 369 | "\u001b[1;31mNameError\u001b[0m: name 'torch' is not defined" 370 | ] 371 | } 372 | ], 373 | "source": [ 374 | "#clearing memory in GPU\n", 375 | "import torch\n", 376 | "torch.cuda.empty_cache()" 377 | ] 378 | }, 379 | { 380 | "cell_type": "code", 381 | "execution_count": null, 382 | "id": "bfad466c", 383 | "metadata": {}, 384 | "outputs": [], 385 | "source": [ 386 | "torch.cuda.memory_summary(device=None, abbreviated=False)" 387 | ] 388 | }, 389 | { 390 | "cell_type": "code", 391 | "execution_count": null, 392 | "id": "2afdc68e", 393 | "metadata": {}, 394 | "outputs": [], 395 | "source": [ 396 | "from platform import python_version\n", 397 | "print(python_version())" 398 | ] 399 | }, 400 | { 401 | "cell_type": "code", 402 | "execution_count": null, 403 | "id": "98c9838b", 404 | "metadata": {}, 405 | "outputs": [], 406 | "source": [] 407 | } 408 | ], 409 | "metadata": { 410 | "kernelspec": { 411 | "display_name": "Python 3", 412 | "language": "python", 413 | "name": "python3" 414 | }, 415 | "language_info": { 416 | "codemirror_mode": { 417 | "name": "ipython", 418 | "version": 3 419 | }, 420 | "file_extension": ".py", 421 | "mimetype": "text/x-python", 422 | "name": "python", 423 | "nbconvert_exporter": "python", 424 | "pygments_lexer": "ipython3", 425 | "version": "3.7.10" 426 | } 427 | }, 428 | "nbformat": 4, 429 | "nbformat_minor": 5 430 | } 431 | -------------------------------------------------------------------------------- /3D_Avatar_Pipeline.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python 2 | 3 | import os 4 | import sys, getopt 5 | import open3d as o3d 6 | import pymeshlab 7 | from glob import glob 8 | 9 | #help usage 10 | if(len(sys.argv)<2): 11 | print("Script Usage: python 3D_Avatar_Pipeline.py ") 12 | sys.exit(0) 13 | 14 | #Visualize the generated mesh(.obj) using PIFuHD 15 | def visualize(mesh): 16 | vis = o3d.visualization.Visualizer() 17 | vis.create_window() 18 | vis.add_geometry(mesh) 19 | vis.run() 20 | vis.destroy_window() 21 | 22 | #remeshing using Quadric Error Metric Decimation by Garland and Heckbert 23 | def remesh(mesh): 24 | return mesh.simplify_quadric_decimation(target_number_of_triangles=14000) 25 | 26 | #<-------------Core Pipeline Begins--------------> 27 | 28 | #read input argument 29 | image_path = str(sys.argv[1]) 30 | filename = os.path.basename(image_path) 31 | filename_raw = filename.rsplit(".",1)[0] 32 | input_directory = os.path.dirname(image_path) 33 | print('Input Image Path:', image_path) 34 | print('Image Name:', filename) 35 | print('Image Directory:', input_directory) 36 | print('Image Raw name:', filename_raw) 37 | #create directory for image 38 | if not os.path.exists('./Results/'+filename_raw): 39 | os.mkdir('./Results/'+filename_raw) 40 | output_directory = './Results/'+filename_raw 41 | print('Output Directory:', output_directory) 42 | 43 | #clean prior pose txt files 44 | #os.system("rm "+input_directory+"\\*.txt") 45 | for file in glob(input_directory+"\*.txt"): 46 | os.remove(file) 47 | 48 | #TODO: pose estimation 49 | print("Executing preprocessing - cropping and pose estimation") 50 | exit_code = os.system("python preprocess_img_pose.py "+image_path) 51 | if exit_code > 0: 52 | print("Error running preprocessing") 53 | sys.exit(exit_code) 54 | os.system("cp ./input/"+filename_raw+"_rect.txt ./Results/"+filename_raw) 55 | 56 | #execute pifuHD as a script 57 | print("Executing PIFuHD") 58 | exit_code = os.system("python -m pifuhd.apps.simple_test --use_rect -i "+input_directory+" -o "+output_directory+" -c ./Checkpoints/pifuhd/pifuhd.pt") 59 | if exit_code > 0: 60 | print("Error running PIFuHD") 61 | sys.exit(exit_code) 62 | 63 | #copy original mesh and rename 64 | os.system("cp "+output_directory+"/pifuhd_final/recon/result_"+filename_raw+"_512.obj "+output_directory) 65 | #if not os.path.exists(output_directory+"/"+filename_raw+"_ori.obj"): 66 | # os.rename(output_directory+"/result_"+filename_raw+"_512.obj", output_directory+"/"+filename_raw+"_ori.obj") 67 | #mesh_filename.replace("result_"+filename_raw+"_512.obj", filename_raw+"_ori.obj") 68 | 69 | #read generate mesh in open3D 70 | mesh = o3d.io.read_triangle_mesh(output_directory+'/result_'+filename_raw+"_512.obj") 71 | #visualize original mesh 72 | print("Visualizing Original Mesh") 73 | visualize(mesh) 74 | 75 | #reduce mesh using Quadratic mesh simplication - to 1k-5k vertices 76 | #print("Remeshing using Quadratic Decimation") 77 | #remesh = remesh(mesh) 78 | #write out the re-meshed obj 79 | #o3d.io.write_triangle_mesh("./Results/suriya_remesh.obj", remesh) 80 | 81 | #reduce mesh using PyMeshLab 82 | print("Remeshing using Simplification: Quadric Edge Collapse Decimation by MeshLab") 83 | ms = pymeshlab.MeshSet() 84 | ms.load_new_mesh(output_directory+'/result_'+filename_raw+"_512.obj") 85 | ms.simplification_quadric_edge_collapse_decimation(targetfacenum = 14000) 86 | ms.save_current_mesh(output_directory+'/'+filename_raw+'_remesh.obj') 87 | 88 | #visualize remesh 89 | remesh = o3d.io.read_triangle_mesh(output_directory+'/'+filename_raw+'_remesh.obj') 90 | print("Visualizing Re-Mesh") 91 | visualize(remesh) 92 | 93 | #TODO: mesh cleaning 94 | 95 | #execute RigNet as a script 96 | print("Executing RigNet") 97 | exit_code = os.system("python rignet_script.py "+filename_raw) 98 | if exit_code > 0: 99 | print("Error running RigNet") 100 | sys.exit(exit_code) 101 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Realistic 3D Avatar Pipeline in Realtime 2 | Realistic 3D Avatar Pipeline using PIFuHD - Pixel Aligned Implicit Functions and RigNet - Neural Rigging Network 3 | 4 | ## Dependency and Setup 5 | This project was tested in Windows 10 x64 System with Python 3.7 | Torch v1.8.0 TorchVision v0.9.0 on CPU with 16Gb of RAM using Anaconda Python Environment. 6 | 7 | ### Constraints 8 | open3D supports only Python v3.6, 3.7 and 3.8 9 | 10 | ``` 11 | conda create -n 3D_Avatar_Pipeline python=3.7 12 | conda activate 3D_Avatar_Pipeline 13 | ``` 14 | 15 | ### Clone the GitHub Repository and the Submodules 16 | ``` 17 | git clone https://github.com/codesavory/3d_avatar_pipeline 18 | git submodule init 19 | git submodule update 20 | ``` 21 | 22 | 23 | ### Install Necessary Libraries 24 | ``` 25 | pip install torch==1.8.0 torchvision==0.9.0 26 | pip install -r requirements.txt 27 | ``` 28 | 29 | ## If using CUDA 10.1 (TO TEST) 30 | ``` 31 | conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=10.2 -c pytorch 32 | ``` 33 | 34 | ### Install some torch depencies 35 | ``` 36 | pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.8.0+cu101.html 37 | pip install torch-sparse -f https://pytorch-geometric.com/whl/torch-1.8.0+cu101.html 38 | pip install torch-cluster -f https://pytorch-geometric.com/whl/torch-1.8.0+cu101.html 39 | pip install torch-spline-conv -f https://pytorch-geometric.com/whl/torch-1.8.0+cu101.html 40 | pip install torch-geometric 41 | ``` 42 | 43 | #### For Windows user 44 | Download Windows-compiled Rtree from [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#rtree), and install it by 45 | `pip install Rtree‑0.9.4‑cp37‑cp37m‑win_amd64.whl` (64-bit system) or 46 | `pip install Rtree‑0.9.4‑cp37‑cp37m‑win32.whl` (32-bit system). Other libraries can be installed in the same way as Linux setup instructions. 47 | 48 | ## Getting the checkpoints 49 | Download the checkpoints into the base folder from [GDrive Link](https://drive.google.com/drive/folders/1mxUAOSpCZHdxcGYUGs9oJRvXkJOXzMEq?usp=sharing) extract and rename it as Checkpoints 50 | 51 | ## Usage 52 | The given script takes input photo(from ./input folder) and stores all the results(to ./Results/). Example usage - 53 | ``` 54 | python 3D_Avatar_Pipeline.py .\input\test.png 55 | ``` 56 | -------------------------------------------------------------------------------- /Results/.gitkeep: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/codesavory/3d_avatar_pipeline/f382bd86b7e881565a309ac8162ba14df7bd43d9/Results/.gitkeep -------------------------------------------------------------------------------- /Rtree-0.9.7-cp37-cp37m-win_amd64.whl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/codesavory/3d_avatar_pipeline/f382bd86b7e881565a309ac8162ba14df7bd43d9/Rtree-0.9.7-cp37-cp37m-win_amd64.whl -------------------------------------------------------------------------------- /binvox: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/codesavory/3d_avatar_pipeline/f382bd86b7e881565a309ac8162ba14df7bd43d9/binvox -------------------------------------------------------------------------------- /binvox.exe: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/codesavory/3d_avatar_pipeline/f382bd86b7e881565a309ac8162ba14df7bd43d9/binvox.exe -------------------------------------------------------------------------------- /input/.gitkeep: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/codesavory/3d_avatar_pipeline/f382bd86b7e881565a309ac8162ba14df7bd43d9/input/.gitkeep -------------------------------------------------------------------------------- /input/test.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/codesavory/3d_avatar_pipeline/f382bd86b7e881565a309ac8162ba14df7bd43d9/input/test.png -------------------------------------------------------------------------------- /preprocess_img_pose.py: -------------------------------------------------------------------------------- 1 | '''cropping image and pose esimation before entering PIFuHD''' 2 | 3 | import torch 4 | import cv2 5 | import numpy as np 6 | from os import path 7 | import sys 8 | sys.path.append(path.abspath('./lightweight-human-pose-estimation.pytorch')) 9 | from models.with_mobilenet import PoseEstimationWithMobileNet 10 | from modules.keypoints import extract_keypoints, group_keypoints 11 | from modules.load_state import load_state 12 | from modules.pose import Pose, track_poses 13 | import demo 14 | import sys 15 | 16 | def get_rect(net, images, height_size): 17 | net = net.eval() 18 | 19 | stride = 8 20 | upsample_ratio = 4 21 | num_keypoints = Pose.num_kpts 22 | previous_poses = [] 23 | delay = 33 24 | for image in images: 25 | #print("image:"+image) 26 | rect_path = image.replace('.%s' % (image.split('.')[-1]), '_rect.txt') 27 | #print("rect_path"+rect_path) 28 | img = cv2.imread(image, cv2.IMREAD_COLOR) 29 | orig_img = img.copy() 30 | orig_img = img.copy() 31 | heatmaps, pafs, scale, pad = demo.infer_fast(net, img, height_size, stride, upsample_ratio, cpu=True) 32 | 33 | total_keypoints_num = 0 34 | all_keypoints_by_type = [] 35 | for kpt_idx in range(num_keypoints): # 19th for bg 36 | total_keypoints_num += extract_keypoints(heatmaps[:, :, kpt_idx], all_keypoints_by_type, total_keypoints_num) 37 | 38 | pose_entries, all_keypoints = group_keypoints(all_keypoints_by_type, pafs) 39 | for kpt_id in range(all_keypoints.shape[0]): 40 | all_keypoints[kpt_id, 0] = (all_keypoints[kpt_id, 0] * stride / upsample_ratio - pad[1]) / scale 41 | all_keypoints[kpt_id, 1] = (all_keypoints[kpt_id, 1] * stride / upsample_ratio - pad[0]) / scale 42 | current_poses = [] 43 | 44 | rects = [] 45 | for n in range(len(pose_entries)): 46 | if len(pose_entries[n]) == 0: 47 | continue 48 | pose_keypoints = np.ones((num_keypoints, 2), dtype=np.int32) * -1 49 | valid_keypoints = [] 50 | for kpt_id in range(num_keypoints): 51 | if pose_entries[n][kpt_id] != -1.0: # keypoint was found 52 | pose_keypoints[kpt_id, 0] = int(all_keypoints[int(pose_entries[n][kpt_id]), 0]) 53 | pose_keypoints[kpt_id, 1] = int(all_keypoints[int(pose_entries[n][kpt_id]), 1]) 54 | valid_keypoints.append([pose_keypoints[kpt_id, 0], pose_keypoints[kpt_id, 1]]) 55 | valid_keypoints = np.array(valid_keypoints) 56 | 57 | if pose_entries[n][10] != -1.0 or pose_entries[n][13] != -1.0: 58 | pmin = valid_keypoints.min(0) 59 | pmax = valid_keypoints.max(0) 60 | 61 | center = (0.5 * (pmax[:2] + pmin[:2])).astype(int) 62 | radius = int(0.65 * max(pmax[0]-pmin[0], pmax[1]-pmin[1])) 63 | 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: 64 | # if leg is missing, use pelvis to get cropping 65 | center = (0.5 * (pose_keypoints[8] + pose_keypoints[11])).astype(np.int) 66 | radius = int(1.45*np.sqrt(((center[None,:] - valid_keypoints)**2).sum(1)).max(0)) 67 | center[1] += int(0.05*radius) 68 | else: 69 | center = np.array([img.shape[1]//2,img.shape[0]//2]) 70 | radius = max(img.shape[1]//2,img.shape[0]//2) 71 | 72 | x1 = center[0] - radius 73 | y1 = center[1] - radius 74 | 75 | rects.append([x1, y1, 2*radius, 2*radius]) 76 | 77 | np.savetxt(rect_path, np.array(rects), fmt='%d') 78 | 79 | net = PoseEstimationWithMobileNet() 80 | checkpoint = torch.load('./Checkpoints/lightweight-human-pose-estimation.pytorch/checkpoint_iter_370000.pth', map_location='cpu') 81 | load_state(net, checkpoint) 82 | 83 | #image_path = './pifuhd/sample_images/%s' % filename 84 | image_path = str(sys.argv[1]) 85 | #print("Input Image:"+str(image_path)) 86 | get_rect(net, [image_path], 512) 87 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | # This is a requirement.txt file 2 | # to install all these requirements: 3 | # `pip install -r requirements.txt` 4 | # Make sure you have pip installed! 5 | # To update just use `pip install -r requirements.txt --upgrade` 6 | # Note: Install in the same order to avoid dependency mismatch 7 | 8 | #Requirements for RigNet 9 | numpy 10 | scipy 11 | matplotlib 12 | tensorboard 13 | open3d==0.9.0 14 | opencv-python 15 | rtree>=0.8,<0.9 16 | trimesh[easy] 17 | 18 | #Visualization 19 | open3d 20 | pymeshlab 21 | pycocotools 22 | 23 | #Requirements for PIFuHD 24 | Pillow # PIL 25 | scikit-image # skimage 26 | tqdm 27 | opencv-python # cv2 28 | trimesh 29 | PyOpenGL 30 | ffmpeg 31 | -------------------------------------------------------------------------------- /rignet_script.py: -------------------------------------------------------------------------------- 1 | # --------------------------------------------------------------------------------------------------------- 2 | # Name: quick_start.py 3 | # Purpose: An easy-to-use demo. Also serves as an interface of the pipeline. 4 | # RigNet Copyright 2020 University of Massachusetts 5 | # RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License. 6 | # Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet. 7 | # --------------------------------------------------------------------------------------------------------- 8 | 9 | from os import path 10 | import sys 11 | sys.path.append(path.abspath('./RigNet')) 12 | 13 | import os 14 | from sys import platform 15 | import trimesh 16 | import numpy as np 17 | import open3d as o3d 18 | import itertools as it 19 | 20 | import torch 21 | from torch_geometric.data import Data 22 | from torch_geometric.utils import add_self_loops 23 | 24 | from utils import binvox_rw 25 | from utils.rig_parser import Skel, Info 26 | from utils.tree_utils import TreeNode 27 | from utils.io_utils import assemble_skel_skin 28 | from utils.vis_utils import draw_shifted_pts, show_obj_skel, show_mesh_vox 29 | from utils.cluster_utils import meanshift_cluster, nms_meanshift 30 | from utils.mst_utils import increase_cost_for_outside_bone, primMST_symmetry, loadSkel_recur, inside_check, flip 31 | 32 | from geometric_proc.common_ops import get_bones, calc_surface_geodesic 33 | from geometric_proc.compute_volumetric_geodesic import pts2line, calc_pts2bone_visible_mat 34 | 35 | from gen_dataset import get_tpl_edges, get_geo_edges 36 | from mst_generate import sample_on_bone, getInitId 37 | from run_skinning import post_filter 38 | 39 | from models.GCN import JOINTNET_MASKNET_MEANSHIFT as JOINTNET 40 | from models.ROOT_GCN import ROOTNET 41 | from models.PairCls_GCN import PairCls as BONENET 42 | from models.SKINNING import SKINNET 43 | 44 | import sys 45 | 46 | def normalize_obj(mesh_v): 47 | dims = [max(mesh_v[:, 0]) - min(mesh_v[:, 0]), 48 | max(mesh_v[:, 1]) - min(mesh_v[:, 1]), 49 | max(mesh_v[:, 2]) - min(mesh_v[:, 2])] 50 | scale = 1.0 / max(dims) 51 | pivot = np.array([(min(mesh_v[:, 0]) + max(mesh_v[:, 0])) / 2, min(mesh_v[:, 1]), 52 | (min(mesh_v[:, 2]) + max(mesh_v[:, 2])) / 2]) 53 | mesh_v[:, 0] -= pivot[0] 54 | mesh_v[:, 1] -= pivot[1] 55 | mesh_v[:, 2] -= pivot[2] 56 | mesh_v *= scale 57 | return mesh_v, pivot, scale 58 | 59 | 60 | def create_single_data(mesh_filaname): 61 | """ 62 | create input data for the network. The data is wrapped by Data structure in pytorch-geometric library 63 | :param mesh_filaname: name of the input mesh 64 | :return: wrapped data, voxelized mesh, and geodesic distance matrix of all vertices 65 | """ 66 | mesh = o3d.io.read_triangle_mesh(mesh_filaname) 67 | mesh.compute_vertex_normals() 68 | mesh_v = np.asarray(mesh.vertices) 69 | mesh_vn = np.asarray(mesh.vertex_normals) 70 | mesh_f = np.asarray(mesh.triangles) 71 | 72 | mesh_v, translation_normalize, scale_normalize = normalize_obj(mesh_v) 73 | mesh_normalized = o3d.geometry.TriangleMesh(vertices=o3d.utility.Vector3dVector(mesh_v), triangles=o3d.utility.Vector3iVector(mesh_f)) 74 | o3d.io.write_triangle_mesh(mesh_filename.replace("_remesh.obj", "_normalized.obj"), mesh_normalized) 75 | 76 | # vertices 77 | v = np.concatenate((mesh_v, mesh_vn), axis=1) 78 | v = torch.from_numpy(v).float() 79 | 80 | # topology edges 81 | print(" gathering topological edges.") 82 | tpl_e = get_tpl_edges(mesh_v, mesh_f).T 83 | tpl_e = torch.from_numpy(tpl_e).long() 84 | tpl_e, _ = add_self_loops(tpl_e, num_nodes=v.size(0)) 85 | 86 | # surface geodesic distance matrix 87 | print(" calculating surface geodesic matrix.") 88 | surface_geodesic = calc_surface_geodesic(mesh) 89 | 90 | # geodesic edges 91 | print(" gathering geodesic edges.") 92 | geo_e = get_geo_edges(surface_geodesic, mesh_v).T 93 | geo_e = torch.from_numpy(geo_e).long() 94 | geo_e, _ = add_self_loops(geo_e, num_nodes=v.size(0)) 95 | 96 | # batch 97 | batch = torch.zeros(len(v), dtype=torch.long) 98 | 99 | # voxel 100 | if not os.path.exists(mesh_filaname.replace('_remesh.obj', '_normalized.binvox')): 101 | if platform == "linux" or platform == "linux2": 102 | os.system("./binvox -d 88 -pb " + mesh_filaname.replace("_remesh.obj", "_normalized.obj")) 103 | elif platform == "win32": 104 | os.system("binvox.exe -d 88 " + mesh_filaname.replace("_remesh.obj", "_normalized.obj")) 105 | else: 106 | raise Exception('Sorry, we currently only support windows and linux.') 107 | 108 | with open(mesh_filaname.replace('_remesh.obj', '_normalized.binvox'), 'rb') as fvox: 109 | vox = binvox_rw.read_as_3d_array(fvox) 110 | 111 | data = Data(x=v[:, 3:6], pos=v[:, 0:3], tpl_edge_index=tpl_e, geo_edge_index=geo_e, batch=batch) 112 | return data, vox, surface_geodesic, translation_normalize, scale_normalize 113 | 114 | 115 | def predict_joints(input_data, vox, joint_pred_net, threshold, bandwidth=None, mesh_filename=None): 116 | """ 117 | Predict joints 118 | :param input_data: wrapped input data 119 | :param vox: voxelized mesh 120 | :param joint_pred_net: network for predicting joints 121 | :param threshold: density threshold to filter out shifted points 122 | :param bandwidth: bandwidth for meanshift clustering 123 | :param mesh_filename: mesh filename for visualization 124 | :return: wrapped data with predicted joints, pair-wise bone representation added. 125 | """ 126 | data_displacement, _, attn_pred, bandwidth_pred = joint_pred_net(input_data) 127 | y_pred = data_displacement + input_data.pos 128 | y_pred_np = y_pred.data.cpu().numpy() 129 | attn_pred_np = attn_pred.data.cpu().numpy() 130 | y_pred_np, index_inside = inside_check(y_pred_np, vox) 131 | attn_pred_np = attn_pred_np[index_inside, :] 132 | y_pred_np = y_pred_np[attn_pred_np.squeeze() > 1e-3] 133 | attn_pred_np = attn_pred_np[attn_pred_np.squeeze() > 1e-3] 134 | 135 | # symmetrize points by reflecting 136 | y_pred_np_reflect = y_pred_np * np.array([[-1, 1, 1]]) 137 | y_pred_np = np.concatenate((y_pred_np, y_pred_np_reflect), axis=0) 138 | attn_pred_np = np.tile(attn_pred_np, (2, 1)) 139 | 140 | #img = draw_shifted_pts(mesh_filename, y_pred_np, weights=attn_pred_np) 141 | if bandwidth is None: 142 | bandwidth = bandwidth_pred.item() 143 | y_pred_np = meanshift_cluster(y_pred_np, bandwidth, attn_pred_np, max_iter=40) 144 | #img = draw_shifted_pts(mesh_filename, y_pred_np, weights=attn_pred_np) 145 | 146 | Y_dist = np.sum(((y_pred_np[np.newaxis, ...] - y_pred_np[:, np.newaxis, :]) ** 2), axis=2) 147 | density = np.maximum(bandwidth ** 2 - Y_dist, np.zeros(Y_dist.shape)) 148 | density = np.sum(density, axis=0) 149 | density_sum = np.sum(density) 150 | y_pred_np = y_pred_np[density / density_sum > threshold] 151 | attn_pred_np = attn_pred_np[density / density_sum > threshold][:, 0] 152 | density = density[density / density_sum > threshold] 153 | 154 | #img = draw_shifted_pts(mesh_filename, y_pred_np, weights=attn_pred_np) 155 | pred_joints = nms_meanshift(y_pred_np, density, bandwidth) 156 | pred_joints, _ = flip(pred_joints) 157 | #img = draw_shifted_pts(mesh_filename, pred_joints) 158 | 159 | # prepare and add new data members 160 | pairs = list(it.combinations(range(pred_joints.shape[0]), 2)) 161 | pair_attr = [] 162 | for pr in pairs: 163 | dist = np.linalg.norm(pred_joints[pr[0]] - pred_joints[pr[1]]) 164 | bone_samples = sample_on_bone(pred_joints[pr[0]], pred_joints[pr[1]]) 165 | bone_samples_inside, _ = inside_check(bone_samples, vox) 166 | outside_proportion = len(bone_samples_inside) / (len(bone_samples) + 1e-10) 167 | attr = np.array([dist, outside_proportion, 1]) 168 | pair_attr.append(attr) 169 | pairs = np.array(pairs) 170 | pair_attr = np.array(pair_attr) 171 | pairs = torch.from_numpy(pairs).float() 172 | pair_attr = torch.from_numpy(pair_attr).float() 173 | pred_joints = torch.from_numpy(pred_joints).float() 174 | joints_batch = torch.zeros(len(pred_joints), dtype=torch.long) 175 | pairs_batch = torch.zeros(len(pairs), dtype=torch.long) 176 | 177 | input_data.joints = pred_joints 178 | input_data.pairs = pairs 179 | input_data.pair_attr = pair_attr 180 | input_data.joints_batch = joints_batch 181 | input_data.pairs_batch = pairs_batch 182 | return input_data 183 | 184 | 185 | def predict_skeleton(input_data, vox, root_pred_net, bone_pred_net, mesh_filename): 186 | """ 187 | Predict skeleton structure based on joints 188 | :param input_data: wrapped data 189 | :param vox: voxelized mesh 190 | :param root_pred_net: network to predict root 191 | :param bone_pred_net: network to predict pairwise connectivity cost 192 | :param mesh_filename: meshfilename for debugging 193 | :return: predicted skeleton structure 194 | """ 195 | root_id = getInitId(input_data, root_pred_net) 196 | pred_joints = input_data.joints.data.cpu().numpy() 197 | 198 | with torch.no_grad(): 199 | connect_prob, _ = bone_pred_net(input_data, permute_joints=False) 200 | connect_prob = torch.sigmoid(connect_prob) 201 | pair_idx = input_data.pairs.long().data.cpu().numpy() 202 | prob_matrix = np.zeros((len(input_data.joints), len(input_data.joints))) 203 | prob_matrix[pair_idx[:, 0], pair_idx[:, 1]] = connect_prob.data.cpu().numpy().squeeze() 204 | prob_matrix = prob_matrix + prob_matrix.transpose() 205 | cost_matrix = -np.log(prob_matrix + 1e-10) 206 | cost_matrix = increase_cost_for_outside_bone(cost_matrix, pred_joints, vox) 207 | 208 | pred_skel = Info() 209 | parent, key, root_id = primMST_symmetry(cost_matrix, root_id, pred_joints) 210 | for i in range(len(parent)): 211 | if parent[i] == -1: 212 | pred_skel.root = TreeNode('root', tuple(pred_joints[i])) 213 | break 214 | loadSkel_recur(pred_skel.root, i, None, pred_joints, parent) 215 | pred_skel.joint_pos = pred_skel.get_joint_dict() 216 | #show_mesh_vox(mesh_filename, vox, pred_skel.root) 217 | try: 218 | img = show_obj_skel(mesh_filename, pred_skel.root) 219 | except: 220 | print("Visualization is not supported on headless servers. Please consider other headless rendering methods.") 221 | return pred_skel 222 | 223 | 224 | def calc_geodesic_matrix(bones, mesh_v, surface_geodesic, mesh_filename, subsampling=False): 225 | """ 226 | calculate volumetric geodesic distance from vertices to each bones 227 | :param bones: B*6 numpy array where each row stores the starting and ending joint position of a bone 228 | :param mesh_v: V*3 mesh vertices 229 | :param surface_geodesic: geodesic distance matrix of all vertices 230 | :param mesh_filename: mesh filename 231 | :return: an approaximate volumetric geodesic distance matrix V*B, were (v,b) is the distance from vertex v to bone b 232 | """ 233 | 234 | if subsampling: 235 | mesh0 = o3d.io.read_triangle_mesh(mesh_filename) 236 | mesh0 = mesh0.simplify_quadric_decimation(3000) 237 | o3d.io.write_triangle_mesh(mesh_filename.replace(".obj", "_simplified.obj"), mesh0) 238 | mesh_trimesh = trimesh.load(mesh_filename.replace(".obj", "_simplified.obj")) 239 | subsamples_ids = np.random.choice(len(mesh_v), np.min((len(mesh_v), 1500)), replace=False) 240 | subsamples = mesh_v[subsamples_ids, :] 241 | surface_geodesic = surface_geodesic[subsamples_ids, :][:, subsamples_ids] 242 | else: 243 | mesh_trimesh = trimesh.load(mesh_filename) 244 | subsamples = mesh_v 245 | origins, ends, pts_bone_dist = pts2line(subsamples, bones) 246 | pts_bone_visibility = calc_pts2bone_visible_mat(mesh_trimesh, origins, ends) 247 | pts_bone_visibility = pts_bone_visibility.reshape(len(bones), len(subsamples)).transpose() 248 | pts_bone_dist = pts_bone_dist.reshape(len(bones), len(subsamples)).transpose() 249 | # remove visible points which are too far 250 | for b in range(pts_bone_visibility.shape[1]): 251 | visible_pts = np.argwhere(pts_bone_visibility[:, b] == 1).squeeze(1) 252 | if len(visible_pts) == 0: 253 | continue 254 | threshold_b = np.percentile(pts_bone_dist[visible_pts, b], 15) 255 | pts_bone_visibility[pts_bone_dist[:, b] > 1.3 * threshold_b, b] = False 256 | 257 | visible_matrix = np.zeros(pts_bone_visibility.shape) 258 | visible_matrix[np.where(pts_bone_visibility == 1)] = pts_bone_dist[np.where(pts_bone_visibility == 1)] 259 | for c in range(visible_matrix.shape[1]): 260 | unvisible_pts = np.argwhere(pts_bone_visibility[:, c] == 0).squeeze(1) 261 | visible_pts = np.argwhere(pts_bone_visibility[:, c] == 1).squeeze(1) 262 | if len(visible_pts) == 0: 263 | visible_matrix[:, c] = pts_bone_dist[:, c] 264 | continue 265 | for r in unvisible_pts: 266 | dist1 = np.min(surface_geodesic[r, visible_pts]) 267 | nn_visible = visible_pts[np.argmin(surface_geodesic[r, visible_pts])] 268 | if np.isinf(dist1): 269 | visible_matrix[r, c] = 8.0 + pts_bone_dist[r, c] 270 | else: 271 | visible_matrix[r, c] = dist1 + visible_matrix[nn_visible, c] 272 | if subsampling: 273 | nn_dist = np.sum((mesh_v[:, np.newaxis, :] - subsamples[np.newaxis, ...])**2, axis=2) 274 | nn_ind = np.argmin(nn_dist, axis=1) 275 | visible_matrix = visible_matrix[nn_ind, :] 276 | os.remove(mesh_filename.replace(".obj", "_simplified.obj")) 277 | return visible_matrix 278 | 279 | 280 | def predict_skinning(input_data, pred_skel, skin_pred_net, surface_geodesic, mesh_filename, subsampling=False): 281 | """ 282 | predict skinning 283 | :param input_data: wrapped input data 284 | :param pred_skel: predicted skeleton 285 | :param skin_pred_net: network to predict skinning weights 286 | :param surface_geodesic: geodesic distance matrix of all vertices 287 | :param mesh_filename: mesh filename 288 | :return: predicted rig with skinning weights information 289 | """ 290 | global device, output_folder 291 | num_nearest_bone = 5 292 | bones, bone_names, bone_isleaf = get_bones(pred_skel) 293 | mesh_v = input_data.pos.data.cpu().numpy() 294 | print(" calculating volumetric geodesic distance from vertices to bone. This step takes some time...") 295 | geo_dist = calc_geodesic_matrix(bones, mesh_v, surface_geodesic, mesh_filename, subsampling=subsampling) 296 | input_samples = [] # joint_pos (x, y, z), (bone_id, 1/D)*5 297 | loss_mask = [] 298 | skin_nn = [] 299 | for v_id in range(len(mesh_v)): 300 | geo_dist_v = geo_dist[v_id] 301 | bone_id_near_to_far = np.argsort(geo_dist_v) 302 | this_sample = [] 303 | this_nn = [] 304 | this_mask = [] 305 | for i in range(num_nearest_bone): 306 | if i >= len(bones): 307 | this_sample += bones[bone_id_near_to_far[0]].tolist() 308 | this_sample.append(1.0 / (geo_dist_v[bone_id_near_to_far[0]] + 1e-10)) 309 | this_sample.append(bone_isleaf[bone_id_near_to_far[0]]) 310 | this_nn.append(0) 311 | this_mask.append(0) 312 | else: 313 | skel_bone_id = bone_id_near_to_far[i] 314 | this_sample += bones[skel_bone_id].tolist() 315 | this_sample.append(1.0 / (geo_dist_v[skel_bone_id] + 1e-10)) 316 | this_sample.append(bone_isleaf[skel_bone_id]) 317 | this_nn.append(skel_bone_id) 318 | this_mask.append(1) 319 | input_samples.append(np.array(this_sample)[np.newaxis, :]) 320 | skin_nn.append(np.array(this_nn)[np.newaxis, :]) 321 | loss_mask.append(np.array(this_mask)[np.newaxis, :]) 322 | 323 | skin_input = np.concatenate(input_samples, axis=0) 324 | loss_mask = np.concatenate(loss_mask, axis=0) 325 | skin_nn = np.concatenate(skin_nn, axis=0) 326 | skin_input = torch.from_numpy(skin_input).float() 327 | input_data.skin_input = skin_input 328 | input_data.to(device) 329 | 330 | skin_pred = skin_pred_net(data) 331 | skin_pred = torch.softmax(skin_pred, dim=1) 332 | skin_pred = skin_pred.data.cpu().numpy() 333 | skin_pred = skin_pred * loss_mask 334 | 335 | skin_nn = skin_nn[:, 0:num_nearest_bone] 336 | skin_pred_full = np.zeros((len(skin_pred), len(bone_names))) 337 | for v in range(len(skin_pred)): 338 | for nn_id in range(len(skin_nn[v, :])): 339 | skin_pred_full[v, skin_nn[v, nn_id]] = skin_pred[v, nn_id] 340 | print(" filtering skinning prediction") 341 | tpl_e = input_data.tpl_edge_index.data.cpu().numpy() 342 | skin_pred_full = post_filter(skin_pred_full, tpl_e, num_ring=1) 343 | skin_pred_full[skin_pred_full < np.max(skin_pred_full, axis=1, keepdims=True) * 0.35] = 0.0 344 | skin_pred_full = skin_pred_full / (skin_pred_full.sum(axis=1, keepdims=True) + 1e-10) 345 | skel_res = assemble_skel_skin(pred_skel, skin_pred_full) 346 | return skel_res 347 | 348 | 349 | def tranfer_to_ori_mesh(filename_ori, filename_remesh, pred_rig): 350 | """ 351 | convert the predicted rig of remeshed model to the rig of the original model. 352 | Just assign skinning weight based on nearest neighbor 353 | :param filename_ori: original mesh filename 354 | :param filename_remesh: remeshed mesh filename 355 | :param pred_rig: predicted rig 356 | :return: predicted rig for original mesh 357 | """ 358 | mesh_remesh = o3d.io.read_triangle_mesh(filename_remesh) 359 | mesh_ori = o3d.io.read_triangle_mesh(filename_ori) 360 | tranfer_rig = Info() 361 | 362 | vert_remesh = np.asarray(mesh_remesh.vertices) 363 | vert_ori = np.asarray(mesh_ori.vertices) 364 | 365 | vertice_distance = np.sqrt(np.sum((vert_ori[np.newaxis, ...] - vert_remesh[:, np.newaxis, :]) ** 2, axis=2)) 366 | vertice_raw_id = np.argmin(vertice_distance, axis=0) # nearest vertex id on the fixed mesh for each vertex on the remeshed mesh 367 | 368 | tranfer_rig.root = pred_rig.root 369 | tranfer_rig.joint_pos = pred_rig.joint_pos 370 | new_skin = [] 371 | for v in range(len(vert_ori)): 372 | skin_v = [v] 373 | v_nn = vertice_raw_id[v] 374 | skin_v += pred_rig.joint_skin[v_nn][1:] 375 | new_skin.append(skin_v) 376 | tranfer_rig.joint_skin = new_skin 377 | return tranfer_rig 378 | 379 | 380 | if __name__ == '__main__': 381 | #root_folder = "" 382 | #input_folder = "meshes/suriya" 383 | 384 | if(len(sys.argv)<2): 385 | print("script usage: python quick_start.py ") 386 | sys.exit(0) 387 | 388 | input_model = str(sys.argv[1]) 389 | input_folder = "Results/"+input_model+'/' 390 | 391 | # downsample_skinning is used to speed up the calculation of volumetric geodesic distance 392 | # and to save cpu memory in skinning calculation. 393 | # Change to False to be more accurate but less efficient. 394 | downsample_skinning = True 395 | 396 | # load all weights 397 | print("loading all networks...") 398 | device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") 399 | print("Device:"+str(device)) 400 | 401 | jointNet = JOINTNET() 402 | jointNet.to(device) 403 | jointNet.eval() 404 | jointNet_checkpoint = torch.load('Checkpoints/Rignet/gcn_meanshift/model_best.pth.tar', map_location = device) 405 | jointNet.load_state_dict(jointNet_checkpoint['state_dict']) 406 | print(" joint prediction network loaded.") 407 | 408 | rootNet = ROOTNET() 409 | rootNet.to(device) 410 | rootNet.eval() 411 | rootNet_checkpoint = torch.load('Checkpoints/Rignet/rootnet/model_best.pth.tar', map_location = device) 412 | rootNet.load_state_dict(rootNet_checkpoint['state_dict']) 413 | print(" root prediction network loaded.") 414 | 415 | boneNet = BONENET() 416 | boneNet.to(device) 417 | boneNet.eval() 418 | boneNet_checkpoint = torch.load('Checkpoints/Rignet/bonenet/model_best.pth.tar', map_location = device) 419 | boneNet.load_state_dict(boneNet_checkpoint['state_dict']) 420 | print(" connection prediction network loaded.") 421 | 422 | skinNet = SKINNET(nearest_bone=5, use_Dg=True, use_Lf=True) 423 | skinNet_checkpoint = torch.load('Checkpoints/Rignet/skinnet/model_best.pth.tar', map_location = device) 424 | skinNet.load_state_dict(skinNet_checkpoint['state_dict']) 425 | skinNet.to(device) 426 | skinNet.eval() 427 | print(" skinning prediction network loaded.") 428 | 429 | # Here we provide 16~17 examples. For best results, we will need to override the learned bandwidth and its associated threshold 430 | # To process other input characters, please first try the learned bandwidth (0.0429 in the provided model), and the default threshold 1e-5. 431 | # We also use these two default parameters for processing all test models in batch. 432 | 433 | #model_id, bandwidth, threshold = "smith", None, 1e-5 434 | model_id, bandwidth, threshold = input_model, 0.045, 0.75e-5 435 | #->model_id, bandwidth, threshold = input_model, 0.05, 1e-5 436 | #model_id, bandwidth, threshold = "8210", 0.05, 1e-5 437 | #model_id, bandwidth, threshold = "8330", 0.05, 0.8e-5 438 | #model_id, bandwidth, threshold = "9477", 0.043, 2.5e-5 439 | #model_id, bandwidth, threshold = "17364", 0.058, 0.3e-5 440 | #model_id, bandwidth, threshold = "15930", 0.055, 0.4e-5 441 | #model_id, bandwidth, threshold = "8333", 0.04, 2e-5 442 | #model_id, bandwidth, threshold = "8338", 0.052, 0.9e-5 443 | #model_id, bandwidth, threshold = "3318", 0.03, 0.92e-5 444 | #model_id, bandwidth, threshold = "15446", 0.032, 0.58e-5 445 | #model_id, bandwidth, threshold = "1347", 0.062, 3e-5 446 | #model_id, bandwidth, threshold = "11814", 0.06, 0.6e-5 447 | #model_id, bandwidth, threshold = "2982", 0.045, 0.3e-5 448 | #model_id, bandwidth, threshold = "2586", 0.05, 0.6e-5 449 | #model_id, bandwidth, threshold = "8184", 0.05, 0.4e-5 450 | #model_id, bandwidth, threshold = "9000", 0.035, 0.16e-5 451 | 452 | # create data used for inferece 453 | print("creating data for model ID {:s}".format(model_id)) 454 | mesh_filename = os.path.join(input_folder, '{:s}_remesh.obj'.format(model_id)) 455 | print("mesh_filename:"+mesh_filename) 456 | data, vox, surface_geodesic, translation_normalize, scale_normalize = create_single_data(mesh_filename) 457 | data.to(device) 458 | 459 | print("predicting joints") 460 | data = predict_joints(data, vox, jointNet, threshold, bandwidth=bandwidth, 461 | mesh_filename=mesh_filename.replace("_remesh.obj", "_normalized.obj")) 462 | data.to(device) 463 | print("predicting connectivity") 464 | pred_skeleton = predict_skeleton(data, vox, rootNet, boneNet, 465 | mesh_filename=mesh_filename.replace("_remesh.obj", "_normalized.obj")) 466 | print("predicting skinning") 467 | pred_rig = predict_skinning(data, pred_skeleton, skinNet, surface_geodesic, 468 | mesh_filename.replace("_remesh.obj", "_normalized.obj"), 469 | subsampling=downsample_skinning) 470 | 471 | # here we reverse the normalization to the original scale and position 472 | pred_rig.normalize(scale_normalize, -translation_normalize) 473 | 474 | print("Saving result") 475 | if os.path.exists(input_folder+'{:s}_ori.obj'.format(model_id)): 476 | # here we use original mesh tesselation (without remeshing) 477 | print("Using original mesh for skin") 478 | mesh_filename_ori = os.path.join(input_folder, '{:s}_ori.obj'.format(model_id)) 479 | pred_rig = tranfer_to_ori_mesh(mesh_filename_ori, mesh_filename, pred_rig) 480 | pred_rig.save(mesh_filename_ori.replace('.obj', '_rig.txt')) 481 | else: 482 | # here we use remeshed mesh 483 | print("Using remesh for skin") 484 | pred_rig.save(mesh_filename.replace('.obj', '_rig.txt')) 485 | print("Done!") 486 | -------------------------------------------------------------------------------- /screenshots/.gitkeep: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/codesavory/3d_avatar_pipeline/f382bd86b7e881565a309ac8162ba14df7bd43d9/screenshots/.gitkeep --------------------------------------------------------------------------------