Spaces:
Runtime error
Runtime error
File size: 79,751 Bytes
487ee6d da48dbe 487ee6d da48dbe 487ee6d da48dbe 487ee6d da48dbe 487ee6d da48dbe fb140f6 da48dbe fb140f6 da48dbe fb140f6 da48dbe 487ee6d fb140f6 da48dbe 487ee6d fb140f6 da48dbe fb140f6 da48dbe fb140f6 da48dbe 487ee6d da48dbe fb140f6 da48dbe 487ee6d da48dbe 487ee6d da48dbe 487ee6d da48dbe 487ee6d da48dbe 487ee6d da48dbe fb140f6 da48dbe fb140f6 da48dbe fb140f6 da48dbe fb140f6 da48dbe fb140f6 da48dbe fb140f6 da48dbe fb140f6 da48dbe fb140f6 da48dbe fb140f6 da48dbe fb140f6 da48dbe 487ee6d da48dbe fb140f6 da48dbe 487ee6d da48dbe fb140f6 da48dbe fb140f6 da48dbe fb140f6 da48dbe fb140f6 da48dbe fb140f6 da48dbe 487ee6d da48dbe fb140f6 487ee6d fb140f6 da48dbe 487ee6d da48dbe 487ee6d fb140f6 487ee6d da48dbe 487ee6d da48dbe 487ee6d da48dbe 487ee6d da48dbe fb140f6 da48dbe fb140f6 da48dbe fb140f6 da48dbe fb140f6 da48dbe fb140f6 da48dbe fb140f6 da48dbe fb140f6 da48dbe fb140f6 487ee6d da48dbe fb140f6 487ee6d da48dbe 487ee6d da48dbe 487ee6d da48dbe fb140f6 da48dbe 487ee6d da48dbe fb140f6 da48dbe 487ee6d da48dbe fb140f6 da48dbe fb140f6 da48dbe 487ee6d da48dbe 487ee6d da48dbe 487ee6d da48dbe 487ee6d da48dbe fb140f6 da48dbe fb140f6 da48dbe fb140f6 da48dbe fb140f6 da48dbe fb140f6 da48dbe fb140f6 da48dbe fb140f6 da48dbe fb140f6 487ee6d fb140f6 da48dbe 487ee6d da48dbe fb140f6 487ee6d fb140f6 da48dbe fb140f6 487ee6d fb140f6 da48dbe 487ee6d da48dbe 487ee6d da48dbe 487ee6d da48dbe fb140f6 487ee6d fb140f6 da48dbe 487ee6d da48dbe 487ee6d da48dbe fb140f6 da48dbe fb140f6 da48dbe fb140f6 da48dbe fb140f6 487ee6d da48dbe fb140f6 da48dbe fb140f6 da48dbe fb140f6 da48dbe 487ee6d da48dbe fb140f6 da48dbe fb140f6 da48dbe fb140f6 da48dbe fb140f6 da48dbe fb140f6 da48dbe |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 |
import logging
import numpy as np
import torch
import torch.nn as nn
from lib.common.config import cfg
from lib.pymafx.core import constants
from lib.pymafx.utils.cam_params import homo_vector
from lib.pymafx.utils.geometry import (
compute_twist_rotation,
projection,
rot6d_to_rotmat,
rotation_matrix_to_angle_axis,
rotmat_to_rot6d,
)
from lib.pymafx.utils.imutils import j2d_processing
from lib.smplx.lbs import batch_rodrigues
from .attention import get_att_block
from .hr_module import get_hrnet_encoder
from .maf_extractor import MAF_Extractor, Mesh_Sampler
from .pose_resnet import get_resnet_encoder
from .res_module import IUV_predict_layer
from .smpl import (
SMPL,
SMPL_MEAN_PARAMS,
SMPL_MODEL_DIR,
SMPL_Family,
get_partial_smpl,
)
logger = logging.getLogger(__name__)
BN_MOMENTUM = 0.1
class Regressor(nn.Module):
def __init__(
self,
feat_dim,
smpl_mean_params,
use_cam_feats=False,
feat_dim_hand=0,
feat_dim_face=0,
bhf_names=['body'],
smpl_models={}
):
super().__init__()
npose = 24 * 6
shape_dim = 10
cam_dim = 3
hand_dim = 15 * 6
face_dim = 3 * 6 + 10
self.body_feat_dim = feat_dim
self.smpl_mode = (cfg.MODEL.MESH_MODEL == 'smpl')
self.smplx_mode = (cfg.MODEL.MESH_MODEL == 'smplx')
self.use_cam_feats = use_cam_feats
cam_feat_len = 4 if self.use_cam_feats else 0
self.bhf_names = bhf_names
self.hand_only_mode = (cfg.TRAIN.BHF_MODE == 'hand_only')
self.face_only_mode = (cfg.TRAIN.BHF_MODE == 'face_only')
self.body_hand_mode = (cfg.TRAIN.BHF_MODE == 'body_hand')
self.full_body_mode = (cfg.TRAIN.BHF_MODE == 'full_body')
# if self.use_cam_feats:
# assert cfg.MODEL.USE_IWP_CAM is False
if 'body' in self.bhf_names:
self.fc1 = nn.Linear(feat_dim + npose + cam_feat_len + shape_dim + cam_dim, 1024)
self.drop1 = nn.Dropout()
self.fc2 = nn.Linear(1024, 1024)
self.drop2 = nn.Dropout()
self.decpose = nn.Linear(1024, npose)
self.decshape = nn.Linear(1024, 10)
self.deccam = nn.Linear(1024, 3)
nn.init.xavier_uniform_(self.decpose.weight, gain=0.01)
nn.init.xavier_uniform_(self.decshape.weight, gain=0.01)
nn.init.xavier_uniform_(self.deccam.weight, gain=0.01)
if not self.smpl_mode:
if self.hand_only_mode:
self.part_names = ['rhand']
elif self.face_only_mode:
self.part_names = ['face']
elif self.body_hand_mode:
self.part_names = ['lhand', 'rhand']
elif self.full_body_mode:
self.part_names = ['lhand', 'rhand', 'face']
else:
self.part_names = []
if 'rhand' in self.part_names:
# self.fc1_hand = nn.Linear(feat_dim_hand + hand_dim + rh_orient_dim + rh_shape_dim + rh_cam_dim, 1024)
self.fc1_hand = nn.Linear(feat_dim_hand + hand_dim, 1024)
self.drop1_hand = nn.Dropout()
self.fc2_hand = nn.Linear(1024, 1024)
self.drop2_hand = nn.Dropout()
# self.declhand = nn.Linear(1024, 15*6)
self.decrhand = nn.Linear(1024, 15 * 6)
# nn.init.xavier_uniform_(self.declhand.weight, gain=0.01)
nn.init.xavier_uniform_(self.decrhand.weight, gain=0.01)
if cfg.MODEL.MESH_MODEL == 'mano' or cfg.MODEL.PyMAF.OPT_WRIST:
rh_cam_dim = 3
rh_orient_dim = 6
rh_shape_dim = 10
self.fc3_hand = nn.Linear(
1024 + rh_orient_dim + rh_shape_dim + rh_cam_dim, 1024
)
self.drop3_hand = nn.Dropout()
self.decshape_rhand = nn.Linear(1024, 10)
self.decorient_rhand = nn.Linear(1024, 6)
self.deccam_rhand = nn.Linear(1024, 3)
nn.init.xavier_uniform_(self.decshape_rhand.weight, gain=0.01)
nn.init.xavier_uniform_(self.decorient_rhand.weight, gain=0.01)
nn.init.xavier_uniform_(self.deccam_rhand.weight, gain=0.01)
if 'face' in self.part_names:
self.fc1_face = nn.Linear(feat_dim_face + face_dim, 1024)
self.drop1_face = nn.Dropout()
self.fc2_face = nn.Linear(1024, 1024)
self.drop2_face = nn.Dropout()
self.dechead = nn.Linear(1024, 3 * 6)
self.decexp = nn.Linear(1024, 10)
nn.init.xavier_uniform_(self.dechead.weight, gain=0.01)
nn.init.xavier_uniform_(self.decexp.weight, gain=0.01)
if cfg.MODEL.MESH_MODEL == 'flame':
rh_cam_dim = 3
rh_orient_dim = 6
rh_shape_dim = 10
self.fc3_face = nn.Linear(
1024 + rh_orient_dim + rh_shape_dim + rh_cam_dim, 1024
)
self.drop3_face = nn.Dropout()
self.decshape_face = nn.Linear(1024, 10)
self.decorient_face = nn.Linear(1024, 6)
self.deccam_face = nn.Linear(1024, 3)
nn.init.xavier_uniform_(self.decshape_face.weight, gain=0.01)
nn.init.xavier_uniform_(self.decorient_face.weight, gain=0.01)
nn.init.xavier_uniform_(self.deccam_face.weight, gain=0.01)
if self.smplx_mode and cfg.MODEL.PyMAF.PRED_VIS_H:
self.fc1_vis = nn.Linear(1024 + 1024 + 1024, 1024)
self.drop1_vis = nn.Dropout()
self.fc2_vis = nn.Linear(1024, 1024)
self.drop2_vis = nn.Dropout()
self.decvis = nn.Linear(1024, 2)
nn.init.xavier_uniform_(self.decvis.weight, gain=0.01)
if 'body' in smpl_models:
self.smpl = smpl_models['body']
if 'hand' in smpl_models:
self.mano = smpl_models['hand']
if 'face' in smpl_models:
self.flame = smpl_models['face']
if cfg.MODEL.PyMAF.OPT_WRIST:
self.body_model = SMPL(model_path=SMPL_MODEL_DIR, batch_size=64, create_transl=False)
mean_params = np.load(smpl_mean_params)
init_pose = torch.from_numpy(mean_params['pose'][:]).unsqueeze(0)
init_shape = torch.from_numpy(mean_params['shape'][:].astype('float32')).unsqueeze(0)
init_cam = torch.from_numpy(mean_params['cam']).unsqueeze(0)
self.register_buffer('init_pose', init_pose)
self.register_buffer('init_shape', init_shape)
self.register_buffer('init_cam', init_cam)
self.register_buffer('init_orient', init_pose[:, :6])
self.flip_vector = torch.ones((1, 9), dtype=torch.float32)
self.flip_vector[:, [1, 2, 3, 6]] *= -1
self.flip_vector = self.flip_vector.reshape(1, 3, 3)
if not self.smpl_mode:
lhand_mean_rot6d = rotmat_to_rot6d(
batch_rodrigues(self.smpl.model.model_neutral.left_hand_mean.view(-1, 3)).view([
-1, 3, 3
])
)
rhand_mean_rot6d = rotmat_to_rot6d(
batch_rodrigues(self.smpl.model.model_neutral.right_hand_mean.view(-1, 3)).view([
-1, 3, 3
])
)
init_lhand = lhand_mean_rot6d.reshape(-1).unsqueeze(0)
init_rhand = rhand_mean_rot6d.reshape(-1).unsqueeze(0)
# init_hand = torch.cat([init_lhand, init_rhand]).unsqueeze(0)
init_face = rotmat_to_rot6d(torch.stack([torch.eye(3)] * 3)).reshape(-1).unsqueeze(0)
init_exp = torch.zeros(10).unsqueeze(0)
if self.smplx_mode or 'hand' in bhf_names:
# init_hand = torch.cat([init_lhand, init_rhand]).unsqueeze(0)
self.register_buffer('init_lhand', init_lhand)
self.register_buffer('init_rhand', init_rhand)
if self.smplx_mode or 'face' in bhf_names:
self.register_buffer('init_face', init_face)
self.register_buffer('init_exp', init_exp)
def forward(
self,
x=None,
n_iter=1,
J_regressor=None,
rw_cam={},
init_mode=False,
global_iter=-1,
**kwargs
):
if x is not None:
batch_size = x.shape[0]
else:
if 'xc_rhand' in kwargs:
batch_size = kwargs['xc_rhand'].shape[0]
elif 'xc_face' in kwargs:
batch_size = kwargs['xc_face'].shape[0]
if 'body' in self.bhf_names:
if 'init_pose' not in kwargs:
kwargs['init_pose'] = self.init_pose.expand(batch_size, -1)
if 'init_shape' not in kwargs:
kwargs['init_shape'] = self.init_shape.expand(batch_size, -1)
if 'init_cam' not in kwargs:
kwargs['init_cam'] = self.init_cam.expand(batch_size, -1)
pred_cam = kwargs['init_cam']
pred_pose = kwargs['init_pose']
pred_shape = kwargs['init_shape']
if self.full_body_mode or self.body_hand_mode:
if cfg.MODEL.PyMAF.OPT_WRIST:
pred_rotmat_body = rot6d_to_rotmat(
pred_pose.reshape(batch_size, -1, 6)
) # .view(batch_size, 24, 3, 3)
if cfg.MODEL.PyMAF.PRED_VIS_H:
pred_vis_hands = None
# if self.full_body_mode or 'hand' in self.bhf_names:
if self.smplx_mode or 'hand' in self.bhf_names:
if 'init_lhand' not in kwargs:
# kwargs['init_lhand'] = self.init_lhand.expand(batch_size, -1)
# init with **right** hand pose
kwargs['init_lhand'] = self.init_rhand.expand(batch_size, -1)
if 'init_rhand' not in kwargs:
kwargs['init_rhand'] = self.init_rhand.expand(batch_size, -1)
pred_lhand, pred_rhand = kwargs['init_lhand'], kwargs['init_rhand']
if cfg.MODEL.MESH_MODEL == 'mano' or cfg.MODEL.PyMAF.OPT_WRIST:
if 'init_orient_rh' not in kwargs:
kwargs['init_orient_rh'] = self.init_orient.expand(batch_size, -1)
if 'init_shape_rh' not in kwargs:
kwargs['init_shape_rh'] = self.init_shape.expand(batch_size, -1)
if 'init_cam_rh' not in kwargs:
kwargs['init_cam_rh'] = self.init_cam.expand(batch_size, -1)
pred_orient_rh = kwargs['init_orient_rh']
pred_shape_rh = kwargs['init_shape_rh']
pred_cam_rh = kwargs['init_cam_rh']
if cfg.MODEL.PyMAF.OPT_WRIST:
if 'init_orient_lh' not in kwargs:
kwargs['init_orient_lh'] = self.init_orient.expand(batch_size, -1)
if 'init_shape_lh' not in kwargs:
kwargs['init_shape_lh'] = self.init_shape.expand(batch_size, -1)
if 'init_cam_lh' not in kwargs:
kwargs['init_cam_lh'] = self.init_cam.expand(batch_size, -1)
pred_orient_lh = kwargs['init_orient_lh']
pred_shape_lh = kwargs['init_shape_lh']
pred_cam_lh = kwargs['init_cam_lh']
if cfg.MODEL.MESH_MODEL == 'mano':
pred_cam = torch.cat([pred_cam_rh[:, 0:1] * 10., pred_cam_rh[:, 1:]], dim=1)
# if self.full_body_mode or 'face' in self.bhf_names:
if self.smplx_mode or 'face' in self.bhf_names:
if 'init_face' not in kwargs:
kwargs['init_face'] = self.init_face.expand(batch_size, -1)
if 'init_hand' not in kwargs:
kwargs['init_exp'] = self.init_exp.expand(batch_size, -1)
pred_face = kwargs['init_face']
pred_exp = kwargs['init_exp']
if cfg.MODEL.MESH_MODEL == 'flame' or cfg.MODEL.PyMAF.OPT_WRIST:
if 'init_orient_fa' not in kwargs:
kwargs['init_orient_fa'] = self.init_orient.expand(batch_size, -1)
pred_orient_fa = kwargs['init_orient_fa']
if 'init_shape_fa' not in kwargs:
kwargs['init_shape_fa'] = self.init_shape.expand(batch_size, -1)
if 'init_cam_fa' not in kwargs:
kwargs['init_cam_fa'] = self.init_cam.expand(batch_size, -1)
pred_shape_fa = kwargs['init_shape_fa']
pred_cam_fa = kwargs['init_cam_fa']
if cfg.MODEL.MESH_MODEL == 'flame':
pred_cam = torch.cat([pred_cam_fa[:, 0:1] * 10., pred_cam_fa[:, 1:]], dim=1)
if not init_mode:
for i in range(n_iter):
if 'body' in self.bhf_names:
xc = torch.cat([x, pred_pose, pred_shape, pred_cam], 1)
if self.use_cam_feats:
if cfg.MODEL.USE_IWP_CAM:
# for IWP camera, simply use pre-defined values
vfov = torch.ones((batch_size, 1)).to(xc) * 0.8
crop_ratio = torch.ones((batch_size, 1)).to(xc) * 0.3
crop_center = torch.ones((batch_size, 2)).to(xc) * 0.5
else:
vfov = rw_cam['vfov'][:, None]
crop_ratio = rw_cam['crop_ratio'][:, None]
crop_center = rw_cam['bbox_center'] / torch.cat([
rw_cam['img_w'][:, None], rw_cam['img_h'][:, None]
], 1)
xc = torch.cat([xc, vfov, crop_ratio, crop_center], 1)
xc = self.fc1(xc)
xc = self.drop1(xc)
xc = self.fc2(xc)
xc = self.drop2(xc)
pred_cam = self.deccam(xc) + pred_cam
pred_pose = self.decpose(xc) + pred_pose
pred_shape = self.decshape(xc) + pred_shape
if not self.smpl_mode:
if self.hand_only_mode:
xc_rhand = kwargs['xc_rhand']
xc_rhand = torch.cat([xc_rhand, pred_rhand], 1)
elif self.face_only_mode:
xc_face = kwargs['xc_face']
xc_face = torch.cat([xc_face, pred_face, pred_exp], 1)
elif self.body_hand_mode:
xc_lhand, xc_rhand = kwargs['xc_lhand'], kwargs['xc_rhand']
xc_lhand = torch.cat([xc_lhand, pred_lhand], 1)
xc_rhand = torch.cat([xc_rhand, pred_rhand], 1)
elif self.full_body_mode:
xc_lhand, xc_rhand, xc_face = kwargs['xc_lhand'], kwargs['xc_rhand'
], kwargs['xc_face']
xc_lhand = torch.cat([xc_lhand, pred_lhand], 1)
xc_rhand = torch.cat([xc_rhand, pred_rhand], 1)
xc_face = torch.cat([xc_face, pred_face, pred_exp], 1)
if 'lhand' in self.part_names:
xc_lhand = self.drop1_hand(self.fc1_hand(xc_lhand))
xc_lhand = self.drop2_hand(self.fc2_hand(xc_lhand))
pred_lhand = self.decrhand(xc_lhand) + pred_lhand
if cfg.MODEL.PyMAF.OPT_WRIST:
xc_lhand = torch.cat([
xc_lhand, pred_shape_lh, pred_orient_lh, pred_cam_lh
], 1)
xc_lhand = self.drop3_hand(self.fc3_hand(xc_lhand))
pred_shape_lh = self.decshape_rhand(xc_lhand) + pred_shape_lh
pred_orient_lh = self.decorient_rhand(xc_lhand) + pred_orient_lh
pred_cam_lh = self.deccam_rhand(xc_lhand) + pred_cam_lh
if 'rhand' in self.part_names:
xc_rhand = self.drop1_hand(self.fc1_hand(xc_rhand))
xc_rhand = self.drop2_hand(self.fc2_hand(xc_rhand))
pred_rhand = self.decrhand(xc_rhand) + pred_rhand
if cfg.MODEL.MESH_MODEL == 'mano' or cfg.MODEL.PyMAF.OPT_WRIST:
xc_rhand = torch.cat([
xc_rhand, pred_shape_rh, pred_orient_rh, pred_cam_rh
], 1)
xc_rhand = self.drop3_hand(self.fc3_hand(xc_rhand))
pred_shape_rh = self.decshape_rhand(xc_rhand) + pred_shape_rh
pred_orient_rh = self.decorient_rhand(xc_rhand) + pred_orient_rh
pred_cam_rh = self.deccam_rhand(xc_rhand) + pred_cam_rh
if cfg.MODEL.MESH_MODEL == 'mano':
pred_cam = torch.cat([
pred_cam_rh[:, 0:1] * 10., pred_cam_rh[:, 1:] / 10.
],
dim=1)
if 'face' in self.part_names:
xc_face = self.drop1_face(self.fc1_face(xc_face))
xc_face = self.drop2_face(self.fc2_face(xc_face))
pred_face = self.dechead(xc_face) + pred_face
pred_exp = self.decexp(xc_face) + pred_exp
if cfg.MODEL.MESH_MODEL == 'flame':
xc_face = torch.cat([
xc_face, pred_shape_fa, pred_orient_fa, pred_cam_fa
], 1)
xc_face = self.drop3_face(self.fc3_face(xc_face))
pred_shape_fa = self.decshape_face(xc_face) + pred_shape_fa
pred_orient_fa = self.decorient_face(xc_face) + pred_orient_fa
pred_cam_fa = self.deccam_face(xc_face) + pred_cam_fa
if cfg.MODEL.MESH_MODEL == 'flame':
pred_cam = torch.cat([
pred_cam_fa[:, 0:1] * 10., pred_cam_fa[:, 1:] / 10.
],
dim=1)
if self.full_body_mode or self.body_hand_mode:
if cfg.MODEL.PyMAF.PRED_VIS_H:
xc_vis = torch.cat([xc, xc_lhand, xc_rhand], 1)
xc_vis = self.drop1_vis(self.fc1_vis(xc_vis))
xc_vis = self.drop2_vis(self.fc2_vis(xc_vis))
pred_vis_hands = self.decvis(xc_vis)
pred_vis_lhand = pred_vis_hands[:, 0] > cfg.MODEL.PyMAF.HAND_VIS_TH
pred_vis_rhand = pred_vis_hands[:, 1] > cfg.MODEL.PyMAF.HAND_VIS_TH
if cfg.MODEL.PyMAF.OPT_WRIST:
pred_rotmat_body = rot6d_to_rotmat(
pred_pose.reshape(batch_size, -1, 6)
) # .view(batch_size, 24, 3, 3)
pred_lwrist = pred_rotmat_body[:, 20]
pred_rwrist = pred_rotmat_body[:, 21]
pred_gl_body, body_joints = self.body_model.get_global_rotation(
global_orient=pred_rotmat_body[:, 0:1],
body_pose=pred_rotmat_body[:, 1:]
)
pred_gl_lelbow = pred_gl_body[:, 18]
pred_gl_relbow = pred_gl_body[:, 19]
target_gl_lwrist = rot6d_to_rotmat(
pred_orient_lh.reshape(batch_size, -1, 6)
)
target_gl_lwrist *= self.flip_vector.to(target_gl_lwrist.device)
target_gl_rwrist = rot6d_to_rotmat(
pred_orient_rh.reshape(batch_size, -1, 6)
)
opt_lwrist = torch.bmm(pred_gl_lelbow.transpose(1, 2), target_gl_lwrist)
opt_rwrist = torch.bmm(pred_gl_relbow.transpose(1, 2), target_gl_rwrist)
if cfg.MODEL.PyMAF.ADAPT_INTEGR:
# if cfg.MODEL.PyMAF.ADAPT_INTEGR and global_iter == (cfg.MODEL.PyMAF.N_ITER - 1):
tpose_joints = self.smpl.get_tpose(betas=pred_shape)
lelbow_twist_axis = nn.functional.normalize(
tpose_joints[:, 20] - tpose_joints[:, 18], dim=1
)
relbow_twist_axis = nn.functional.normalize(
tpose_joints[:, 21] - tpose_joints[:, 19], dim=1
)
lelbow_twist, lelbow_twist_angle = compute_twist_rotation(
opt_lwrist, lelbow_twist_axis
)
relbow_twist, relbow_twist_angle = compute_twist_rotation(
opt_rwrist, relbow_twist_axis
)
min_angle = -0.4 * float(np.pi)
max_angle = 0.4 * float(np.pi)
lelbow_twist_angle[lelbow_twist_angle == torch.
clamp(lelbow_twist_angle, min_angle, max_angle)
] = 0
relbow_twist_angle[relbow_twist_angle == torch.
clamp(relbow_twist_angle, min_angle, max_angle)
] = 0
lelbow_twist_angle[lelbow_twist_angle > max_angle] -= max_angle
lelbow_twist_angle[lelbow_twist_angle < min_angle] -= min_angle
relbow_twist_angle[relbow_twist_angle > max_angle] -= max_angle
relbow_twist_angle[relbow_twist_angle < min_angle] -= min_angle
lelbow_twist = batch_rodrigues(
lelbow_twist_axis * lelbow_twist_angle
)
relbow_twist = batch_rodrigues(
relbow_twist_axis * relbow_twist_angle
)
opt_lwrist = torch.bmm(lelbow_twist.transpose(1, 2), opt_lwrist)
opt_rwrist = torch.bmm(relbow_twist.transpose(1, 2), opt_rwrist)
# left elbow: 18
opt_lelbow = torch.bmm(pred_rotmat_body[:, 18], lelbow_twist)
# right elbow: 19
opt_relbow = torch.bmm(pred_rotmat_body[:, 19], relbow_twist)
if cfg.MODEL.PyMAF.PRED_VIS_H and global_iter == (
cfg.MODEL.PyMAF.N_ITER - 1
):
opt_lwrist_filtered = [
opt_lwrist[_i]
if pred_vis_lhand[_i] else pred_rotmat_body[_i, 20]
for _i in range(batch_size)
]
opt_rwrist_filtered = [
opt_rwrist[_i]
if pred_vis_rhand[_i] else pred_rotmat_body[_i, 21]
for _i in range(batch_size)
]
opt_lelbow_filtered = [
opt_lelbow[_i]
if pred_vis_lhand[_i] else pred_rotmat_body[_i, 18]
for _i in range(batch_size)
]
opt_relbow_filtered = [
opt_relbow[_i]
if pred_vis_rhand[_i] else pred_rotmat_body[_i, 19]
for _i in range(batch_size)
]
opt_lwrist = torch.stack(opt_lwrist_filtered)
opt_rwrist = torch.stack(opt_rwrist_filtered)
opt_lelbow = torch.stack(opt_lelbow_filtered)
opt_relbow = torch.stack(opt_relbow_filtered)
pred_rotmat_body = torch.cat([
pred_rotmat_body[:, :18],
opt_lelbow.unsqueeze(1),
opt_relbow.unsqueeze(1),
opt_lwrist.unsqueeze(1),
opt_rwrist.unsqueeze(1), pred_rotmat_body[:, 22:]
], 1)
else:
if cfg.MODEL.PyMAF.PRED_VIS_H and global_iter == (
cfg.MODEL.PyMAF.N_ITER - 1
):
opt_lwrist_filtered = [
opt_lwrist[_i]
if pred_vis_lhand[_i] else pred_rotmat_body[_i, 20]
for _i in range(batch_size)
]
opt_rwrist_filtered = [
opt_rwrist[_i]
if pred_vis_rhand[_i] else pred_rotmat_body[_i, 21]
for _i in range(batch_size)
]
opt_lwrist = torch.stack(opt_lwrist_filtered)
opt_rwrist = torch.stack(opt_rwrist_filtered)
pred_rotmat_body = torch.cat([
pred_rotmat_body[:, :20],
opt_lwrist.unsqueeze(1),
opt_rwrist.unsqueeze(1), pred_rotmat_body[:, 22:]
], 1)
if self.hand_only_mode:
pred_rotmat_rh = rot6d_to_rotmat(
torch.cat([pred_orient_rh, pred_rhand], dim=1).reshape(batch_size, -1, 6)
) # .view(batch_size, 16, 3, 3)
assert pred_rotmat_rh.shape[1] == 1 + 15
elif self.face_only_mode:
pred_rotmat_fa = rot6d_to_rotmat(
torch.cat([pred_orient_fa, pred_face], dim=1).reshape(batch_size, -1, 6)
) # .view(batch_size, 16, 3, 3)
assert pred_rotmat_fa.shape[1] == 1 + 3
elif self.full_body_mode or self.body_hand_mode:
if cfg.MODEL.PyMAF.OPT_WRIST:
pred_rotmat = pred_rotmat_body
else:
pred_rotmat = rot6d_to_rotmat(
pred_pose.reshape(batch_size, -1, 6)
) # .view(batch_size, 24, 3, 3)
assert pred_rotmat.shape[1] == 24
else:
pred_rotmat = rot6d_to_rotmat(
pred_pose.reshape(batch_size, -1, 6)
) # .view(batch_size, 24, 3, 3)
assert pred_rotmat.shape[1] == 24
# if self.full_body_mode:
if self.smplx_mode:
if cfg.MODEL.PyMAF.PRED_VIS_H and global_iter == (cfg.MODEL.PyMAF.N_ITER - 1):
pred_lhand_filtered = [
pred_lhand[_i] if pred_vis_lhand[_i] else self.init_rhand[0]
for _i in range(batch_size)
]
pred_rhand_filtered = [
pred_rhand[_i] if pred_vis_rhand[_i] else self.init_rhand[0]
for _i in range(batch_size)
]
pred_lhand_filtered = torch.stack(pred_lhand_filtered)
pred_rhand_filtered = torch.stack(pred_rhand_filtered)
pred_hf6d = torch.cat([pred_lhand_filtered, pred_rhand_filtered, pred_face],
dim=1).reshape(batch_size, -1, 6)
else:
pred_hf6d = torch.cat([pred_lhand, pred_rhand, pred_face],
dim=1).reshape(batch_size, -1, 6)
pred_hfrotmat = rot6d_to_rotmat(pred_hf6d)
assert pred_hfrotmat.shape[1] == (15 * 2 + 3)
# flip left hand pose
pred_lhand_rotmat = pred_hfrotmat[:, :15] * self.flip_vector.to(pred_hfrotmat.device
).unsqueeze(0)
pred_rhand_rotmat = pred_hfrotmat[:, 15:30]
pred_face_rotmat = pred_hfrotmat[:, 30:]
if self.hand_only_mode:
pred_output = self.mano(
betas=pred_shape_rh,
right_hand_pose=pred_rotmat_rh[:, 1:],
global_orient=pred_rotmat_rh[:, 0].unsqueeze(1),
pose2rot=False,
)
elif self.face_only_mode:
pred_output = self.flame(
betas=pred_shape_fa,
global_orient=pred_rotmat_fa[:, 0].unsqueeze(1),
jaw_pose=pred_rotmat_fa[:, 1:2],
leye_pose=pred_rotmat_fa[:, 2:3],
reye_pose=pred_rotmat_fa[:, 3:4],
expression=pred_exp,
pose2rot=False,
)
else:
smplx_kwargs = {}
# if self.full_body_mode:
if self.smplx_mode:
smplx_kwargs['left_hand_pose'] = pred_lhand_rotmat
smplx_kwargs['right_hand_pose'] = pred_rhand_rotmat
smplx_kwargs['jaw_pose'] = pred_face_rotmat[:, 0:1]
smplx_kwargs['leye_pose'] = pred_face_rotmat[:, 1:2]
smplx_kwargs['reye_pose'] = pred_face_rotmat[:, 2:3]
smplx_kwargs['expression'] = pred_exp
pred_output = self.smpl(
betas=pred_shape,
body_pose=pred_rotmat[:, 1:],
global_orient=pred_rotmat[:, 0].unsqueeze(1),
pose2rot=False,
**smplx_kwargs,
)
pred_vertices = pred_output.vertices
pred_joints = pred_output.joints
if self.hand_only_mode:
pred_joints_full = pred_output.rhand_joints
elif self.face_only_mode:
pred_joints_full = pred_output.face_joints
elif self.smplx_mode:
pred_joints_full = torch.cat([
pred_joints, pred_output.lhand_joints, pred_output.rhand_joints,
pred_output.face_joints, pred_output.lfoot_joints, pred_output.rfoot_joints
],
dim=1)
else:
pred_joints_full = pred_joints
pred_keypoints_2d = projection(
pred_joints_full, {**rw_cam, 'cam_sxy': pred_cam}, iwp_mode=cfg.MODEL.USE_IWP_CAM
)
if cfg.MODEL.USE_IWP_CAM:
# Normalize keypoints to [-1,1]
pred_keypoints_2d = pred_keypoints_2d / (224. / 2.)
else:
pred_keypoints_2d = j2d_processing(pred_keypoints_2d, rw_cam['kps_transf'])
len_b_kp = len(constants.JOINT_NAMES)
output = {}
if self.smpl_mode or self.smplx_mode:
if J_regressor is not None:
kp_3d = torch.matmul(J_regressor, pred_vertices)
pred_pelvis = kp_3d[:, [0], :].clone()
kp_3d = kp_3d[:, constants.H36M_TO_J14, :]
kp_3d = kp_3d - pred_pelvis
else:
kp_3d = pred_joints
pose = rotation_matrix_to_angle_axis(pred_rotmat.reshape(-1, 3, 3)).reshape(-1, 72)
output.update({
'theta': torch.cat([pred_cam, pred_shape, pose], dim=1),
'verts': pred_vertices,
'kp_2d': pred_keypoints_2d[:, :len_b_kp],
'kp_3d': kp_3d,
'pred_joints': pred_joints,
'smpl_kp_3d': pred_output.smpl_joints,
'rotmat': pred_rotmat,
'pred_cam': pred_cam,
'pred_shape': pred_shape,
'pred_pose': pred_pose,
})
# if self.full_body_mode:
if self.smplx_mode:
# assert pred_keypoints_2d.shape[1] == 144
len_h_kp = len(constants.HAND_NAMES)
len_f_kp = len(constants.FACIAL_LANDMARKS)
len_feet_kp = 2 * len(constants.FOOT_NAMES)
output.update({
'smplx_verts':
pred_output.smplx_vertices if cfg.MODEL.EVAL_MODE else None,
'pred_lhand':
pred_lhand,
'pred_rhand':
pred_rhand,
'pred_face':
pred_face,
'pred_exp':
pred_exp,
'verts_lh':
pred_output.lhand_vertices,
'verts_rh':
pred_output.rhand_vertices,
# 'pred_arm_rotmat': pred_arm_rotmat,
# 'pred_hfrotmat': pred_hfrotmat,
'pred_lhand_rotmat':
pred_lhand_rotmat,
'pred_rhand_rotmat':
pred_rhand_rotmat,
'pred_face_rotmat':
pred_face_rotmat,
'pred_lhand_kp3d':
pred_output.lhand_joints,
'pred_rhand_kp3d':
pred_output.rhand_joints,
'pred_face_kp3d':
pred_output.face_joints,
'pred_lhand_kp2d':
pred_keypoints_2d[:, len_b_kp:len_b_kp + len_h_kp],
'pred_rhand_kp2d':
pred_keypoints_2d[:, len_b_kp + len_h_kp:len_b_kp + len_h_kp * 2],
'pred_face_kp2d':
pred_keypoints_2d[:,
len_b_kp + len_h_kp * 2:len_b_kp + len_h_kp * 2 + len_f_kp],
'pred_feet_kp2d':
pred_keypoints_2d[:, len_b_kp + len_h_kp * 2 + len_f_kp:len_b_kp +
len_h_kp * 2 + len_f_kp + len_feet_kp],
})
if cfg.MODEL.PyMAF.OPT_WRIST:
output.update({
'pred_orient_lh': pred_orient_lh,
'pred_shape_lh': pred_shape_lh,
'pred_orient_rh': pred_orient_rh,
'pred_shape_rh': pred_shape_rh,
'pred_cam_fa': pred_cam_fa,
'pred_cam_lh': pred_cam_lh,
'pred_cam_rh': pred_cam_rh,
})
if cfg.MODEL.PyMAF.PRED_VIS_H:
output.update({'pred_vis_hands': pred_vis_hands})
elif self.hand_only_mode:
# hand mesh out
assert pred_keypoints_2d.shape[1] == 21
output.update({
'theta': pred_cam,
'pred_cam': pred_cam,
'pred_rhand': pred_rhand,
'pred_rhand_rotmat': pred_rotmat_rh[:, 1:],
'pred_orient_rh': pred_orient_rh,
'pred_orient_rh_rotmat': pred_rotmat_rh[:, 0],
'verts_rh': pred_output.rhand_vertices,
'pred_cam_rh': pred_cam_rh,
'pred_shape_rh': pred_shape_rh,
'pred_rhand_kp3d': pred_output.rhand_joints,
'pred_rhand_kp2d': pred_keypoints_2d,
})
elif self.face_only_mode:
# face mesh out
assert pred_keypoints_2d.shape[1] == 68
output.update({
'theta': pred_cam,
'pred_cam': pred_cam,
'pred_face': pred_face,
'pred_exp': pred_exp,
'pred_face_rotmat': pred_rotmat_fa[:, 1:],
'pred_orient_fa': pred_orient_fa,
'pred_orient_fa_rotmat': pred_rotmat_fa[:, 0],
'verts_fa': pred_output.flame_vertices,
'pred_cam_fa': pred_cam_fa,
'pred_shape_fa': pred_shape_fa,
'pred_face_kp3d': pred_output.face_joints,
'pred_face_kp2d': pred_keypoints_2d,
})
return output
def get_attention_modules(
module_keys, img_feature_dim_list, hidden_feat_dim, n_iter, num_attention_heads=1
):
align_attention = nn.ModuleDict()
for k in module_keys:
align_attention[k] = nn.ModuleList()
for i in range(n_iter):
align_attention[k].append(
get_att_block(
img_feature_dim=img_feature_dim_list[k][i],
hidden_feat_dim=hidden_feat_dim,
num_attention_heads=num_attention_heads
)
)
return align_attention
def get_fusion_modules(module_keys, ma_feat_dim, grid_feat_dim, n_iter, out_feat_len):
feat_fusion = nn.ModuleDict()
for k in module_keys:
feat_fusion[k] = nn.ModuleList()
for i in range(n_iter):
feat_fusion[k].append(nn.Linear(grid_feat_dim + ma_feat_dim[k], out_feat_len[k]))
return feat_fusion
class PyMAF(nn.Module):
""" PyMAF based Regression Network for Human Mesh Recovery / Full-body Mesh Recovery
PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop, in ICCV, 2021
PyMAF-X: Towards Well-aligned Full-body Model Regression from Monocular Images, arXiv:2207.06400, 2022
"""
def __init__(
self, smpl_mean_params=SMPL_MEAN_PARAMS, pretrained=True, device=torch.device('cuda')
):
super().__init__()
self.device = device
self.smpl_mode = (cfg.MODEL.MESH_MODEL == 'smpl')
self.smplx_mode = (cfg.MODEL.MESH_MODEL == 'smplx')
assert cfg.TRAIN.BHF_MODE in [
'body_only', 'hand_only', 'face_only', 'body_hand', 'full_body'
]
self.hand_only_mode = (cfg.TRAIN.BHF_MODE == 'hand_only')
self.face_only_mode = (cfg.TRAIN.BHF_MODE == 'face_only')
self.body_hand_mode = (cfg.TRAIN.BHF_MODE == 'body_hand')
self.full_body_mode = (cfg.TRAIN.BHF_MODE == 'full_body')
bhf_names = []
if cfg.TRAIN.BHF_MODE in ['body_only', 'body_hand', 'full_body']:
bhf_names.append('body')
if cfg.TRAIN.BHF_MODE in ['hand_only', 'body_hand', 'full_body']:
bhf_names.append('hand')
if cfg.TRAIN.BHF_MODE in ['face_only', 'full_body']:
bhf_names.append('face')
self.bhf_names = bhf_names
self.part_module_names = {'body': {}, 'hand': {}, 'face': {}, 'link': {}}
# the limb parts need to be handled
if self.hand_only_mode:
self.part_names = ['rhand']
elif self.face_only_mode:
self.part_names = ['face']
elif self.body_hand_mode:
self.part_names = ['lhand', 'rhand']
elif self.full_body_mode:
self.part_names = ['lhand', 'rhand', 'face']
else:
self.part_names = []
# joint index info
if not self.smpl_mode:
h_root_idx = constants.HAND_NAMES.index('wrist')
h_idx = constants.HAND_NAMES.index('middle1')
f_idx = constants.FACIAL_LANDMARKS.index('nose_middle')
self.hf_center_idx = {'lhand': h_idx, 'rhand': h_idx, 'face': f_idx}
self.hf_root_idx = {'lhand': h_root_idx, 'rhand': h_root_idx, 'face': f_idx}
lh_idx_coco = constants.COCO_KEYPOINTS.index('left_wrist')
rh_idx_coco = constants.COCO_KEYPOINTS.index('right_wrist')
f_idx_coco = constants.COCO_KEYPOINTS.index('nose')
self.hf_root_idx_coco = {'lhand': lh_idx_coco, 'rhand': rh_idx_coco, 'face': f_idx_coco}
# create parametric mesh models
self.smpl_family = {}
if self.hand_only_mode and cfg.MODEL.MESH_MODEL == 'mano':
self.smpl_family['hand'] = SMPL_Family(model_type='mano')
self.smpl_family['body'] = SMPL_Family(model_type='smplx')
elif self.face_only_mode and cfg.MODEL.MESH_MODEL == 'flame':
self.smpl_family['face'] = SMPL_Family(model_type='flame')
self.smpl_family['body'] = SMPL_Family(model_type='smplx')
else:
self.smpl_family['body'] = SMPL_Family(
model_type=cfg.MODEL.MESH_MODEL, all_gender=cfg.MODEL.ALL_GENDER
)
self.init_mesh_output = None
self.batch_size = 1
self.encoders = nn.ModuleDict()
self.global_mode = not cfg.MODEL.PyMAF.MAF_ON
# build encoders
global_feat_dim = 2048
bhf_ma_feat_dim = {}
# encoder for the body part
if 'body' in bhf_names:
# if self.smplx_mode or 'hr' in cfg.MODEL.PyMAF.BACKBONE:
if cfg.MODEL.PyMAF.BACKBONE == 'res50':
body_encoder = get_resnet_encoder(
cfg, init_weight=(not cfg.MODEL.EVAL_MODE), global_mode=self.global_mode
)
body_sfeat_dim = list(cfg.POSE_RES_MODEL.EXTRA.NUM_DECONV_FILTERS)
elif cfg.MODEL.PyMAF.BACKBONE == 'hr48':
body_encoder = get_hrnet_encoder(
cfg, init_weight=(not cfg.MODEL.EVAL_MODE), global_mode=self.global_mode
)
body_sfeat_dim = list(cfg.HR_MODEL.EXTRA.STAGE4.NUM_CHANNELS)
body_sfeat_dim.reverse()
body_sfeat_dim = body_sfeat_dim[1:]
else:
raise NotImplementedError
self.encoders['body'] = body_encoder
self.part_module_names['body'].update({'encoders.body': self.encoders['body']})
self.mesh_sampler = Mesh_Sampler(type='smpl')
self.part_module_names['body'].update({'mesh_sampler': self.mesh_sampler})
if not cfg.MODEL.PyMAF.GRID_FEAT:
ma_feat_dim = self.mesh_sampler.Dmap.shape[0] * cfg.MODEL.PyMAF.MLP_DIM[-1]
else:
ma_feat_dim = 0
bhf_ma_feat_dim['body'] = ma_feat_dim
dp_feat_dim = body_sfeat_dim[-1]
self.with_uv = cfg.LOSS.POINT_REGRESSION_WEIGHTS > 0
if cfg.MODEL.PyMAF.AUX_SUPV_ON:
assert cfg.MODEL.PyMAF.MAF_ON
self.dp_head = IUV_predict_layer(feat_dim=dp_feat_dim)
self.part_module_names['body'].update({'dp_head': self.dp_head})
# encoders for the hand / face parts
if 'hand' in self.bhf_names or 'face' in self.bhf_names:
for hf in ['hand', 'face']:
if hf in bhf_names:
if cfg.MODEL.PyMAF.HF_BACKBONE == 'res50':
self.encoders[hf] = get_resnet_encoder(
cfg,
init_weight=(not cfg.MODEL.EVAL_MODE),
global_mode=self.global_mode
)
self.part_module_names[hf].update({f'encoders.{hf}': self.encoders[hf]})
hf_sfeat_dim = list(cfg.POSE_RES_MODEL.EXTRA.NUM_DECONV_FILTERS)
else:
raise NotImplementedError
if cfg.MODEL.PyMAF.HF_AUX_SUPV_ON:
assert cfg.MODEL.PyMAF.MAF_ON
self.dp_head_hf = nn.ModuleDict()
if 'hand' in bhf_names:
self.dp_head_hf['hand'] = IUV_predict_layer(
feat_dim=hf_sfeat_dim[-1], mode='pncc'
)
self.part_module_names['hand'].update({
'dp_head_hf.hand': self.dp_head_hf['hand']
})
if 'face' in bhf_names:
self.dp_head_hf['face'] = IUV_predict_layer(
feat_dim=hf_sfeat_dim[-1], mode='pncc'
)
self.part_module_names['face'].update({
'dp_head_hf.face': self.dp_head_hf['face']
})
smpl2limb_vert_faces = get_partial_smpl()
self.smpl2lhand = torch.from_numpy(smpl2limb_vert_faces['lhand']['vids']).long()
self.smpl2rhand = torch.from_numpy(smpl2limb_vert_faces['rhand']['vids']).long()
# grid points for grid feature extraction
grid_size = 21
xv, yv = torch.meshgrid([
torch.linspace(-1, 1, grid_size),
torch.linspace(-1, 1, grid_size)
])
grid_points = torch.stack([xv.reshape(-1), yv.reshape(-1)]).unsqueeze(0)
self.register_buffer('grid_points', grid_points)
grid_feat_dim = grid_size * grid_size * cfg.MODEL.PyMAF.MLP_DIM[-1]
# the fusion of grid and mesh-aligned features
self.fuse_grid_align = cfg.MODEL.PyMAF.GRID_ALIGN.USE_ATT or cfg.MODEL.PyMAF.GRID_ALIGN.USE_FC
assert not (cfg.MODEL.PyMAF.GRID_ALIGN.USE_ATT and cfg.MODEL.PyMAF.GRID_ALIGN.USE_FC)
if self.fuse_grid_align:
self.att_starts = cfg.MODEL.PyMAF.GRID_ALIGN.ATT_STARTS
n_iter_att = cfg.MODEL.PyMAF.N_ITER - self.att_starts
att_feat_dim_idx = -cfg.MODEL.PyMAF.GRID_ALIGN.ATT_FEAT_IDX
num_att_heads = cfg.MODEL.PyMAF.GRID_ALIGN.ATT_HEAD
hidden_feat_dim = cfg.MODEL.PyMAF.MLP_DIM[att_feat_dim_idx]
bhf_att_feat_dim = {'body': 2048}
if 'hand' in self.bhf_names:
self.mano_sampler = Mesh_Sampler(type='mano', level=1)
self.mano_ds_len = self.mano_sampler.Dmap.shape[0]
self.part_module_names['hand'].update({'mano_sampler': self.mano_sampler})
bhf_ma_feat_dim.update({'hand': self.mano_ds_len * cfg.MODEL.PyMAF.HF_MLP_DIM[-1]})
if self.fuse_grid_align:
bhf_att_feat_dim.update({'hand': 1024})
if 'face' in self.bhf_names:
bhf_ma_feat_dim.update({
'face':
len(constants.FACIAL_LANDMARKS) * cfg.MODEL.PyMAF.HF_MLP_DIM[-1]
})
if self.fuse_grid_align:
bhf_att_feat_dim.update({'face': 1024})
# spatial alignment attention
if cfg.MODEL.PyMAF.GRID_ALIGN.USE_ATT:
hfimg_feat_dim_list = {}
if 'body' in bhf_names:
hfimg_feat_dim_list['body'] = body_sfeat_dim[-n_iter_att:]
if 'hand' in self.bhf_names or 'face' in self.bhf_names:
if 'hand' in bhf_names:
hfimg_feat_dim_list['hand'] = hf_sfeat_dim[-n_iter_att:]
if 'face' in bhf_names:
hfimg_feat_dim_list['face'] = hf_sfeat_dim[-n_iter_att:]
self.align_attention = get_attention_modules(
bhf_names,
hfimg_feat_dim_list,
hidden_feat_dim,
n_iter=n_iter_att,
num_attention_heads=num_att_heads
)
for part in bhf_names:
self.part_module_names[part].update({
f'align_attention.{part}':
self.align_attention[part]
})
if self.fuse_grid_align:
self.att_feat_reduce = get_fusion_modules(
bhf_names,
bhf_ma_feat_dim,
grid_feat_dim,
n_iter=n_iter_att,
out_feat_len=bhf_att_feat_dim
)
for part in bhf_names:
self.part_module_names[part].update({
f'att_feat_reduce.{part}':
self.att_feat_reduce[part]
})
# build regressor for parameter prediction
self.regressor = nn.ModuleList()
for i in range(cfg.MODEL.PyMAF.N_ITER):
ref_infeat_dim = 0
if 'body' in self.bhf_names:
if cfg.MODEL.PyMAF.MAF_ON:
if self.fuse_grid_align:
if i >= self.att_starts:
ref_infeat_dim = bhf_att_feat_dim['body']
elif i == 0 or cfg.MODEL.PyMAF.GRID_FEAT:
ref_infeat_dim = grid_feat_dim
else:
ref_infeat_dim = ma_feat_dim
else:
if i == 0 or cfg.MODEL.PyMAF.GRID_FEAT:
ref_infeat_dim = grid_feat_dim
else:
ref_infeat_dim = ma_feat_dim
else:
ref_infeat_dim = global_feat_dim
if self.smpl_mode:
self.regressor.append(
Regressor(
feat_dim=ref_infeat_dim,
smpl_mean_params=smpl_mean_params,
use_cam_feats=cfg.MODEL.PyMAF.USE_CAM_FEAT,
smpl_models=self.smpl_family
)
)
else:
if cfg.MODEL.PyMAF.MAF_ON:
if 'hand' in self.bhf_names or 'face' in self.bhf_names:
if i == 0:
feat_dim_hand = grid_feat_dim if 'hand' in self.bhf_names else None
feat_dim_face = grid_feat_dim if 'face' in self.bhf_names else None
else:
if self.fuse_grid_align:
feat_dim_hand = bhf_att_feat_dim[
'hand'] if 'hand' in self.bhf_names else None
feat_dim_face = bhf_att_feat_dim[
'face'] if 'face' in self.bhf_names else None
else:
feat_dim_hand = bhf_ma_feat_dim[
'hand'] if 'hand' in self.bhf_names else None
feat_dim_face = bhf_ma_feat_dim[
'face'] if 'face' in self.bhf_names else None
else:
feat_dim_hand = ref_infeat_dim
feat_dim_face = ref_infeat_dim
else:
ref_infeat_dim = global_feat_dim
feat_dim_hand = global_feat_dim
feat_dim_face = global_feat_dim
self.regressor.append(
Regressor(
feat_dim=ref_infeat_dim,
smpl_mean_params=smpl_mean_params,
use_cam_feats=cfg.MODEL.PyMAF.USE_CAM_FEAT,
feat_dim_hand=feat_dim_hand,
feat_dim_face=feat_dim_face,
bhf_names=bhf_names,
smpl_models=self.smpl_family
)
)
# assign sub-regressor to each part
for dec_name, dec_module in self.regressor[-1].named_children():
if 'hand' in dec_name:
self.part_module_names['hand'].update({
'regressor.{}.{}.'.format(len(self.regressor) - 1, dec_name):
dec_module
})
elif 'face' in dec_name or 'head' in dec_name or 'exp' in dec_name:
self.part_module_names['face'].update({
'regressor.{}.{}.'.format(len(self.regressor) - 1, dec_name):
dec_module
})
elif 'res' in dec_name or 'vis' in dec_name:
self.part_module_names['link'].update({
'regressor.{}.{}.'.format(len(self.regressor) - 1, dec_name):
dec_module
})
elif 'body' in self.part_module_names:
self.part_module_names['body'].update({
'regressor.{}.{}.'.format(len(self.regressor) - 1, dec_name):
dec_module
})
# mesh-aligned feature extractor
self.maf_extractor = nn.ModuleDict()
for part in bhf_names:
self.maf_extractor[part] = nn.ModuleList()
filter_channels_default = cfg.MODEL.PyMAF.MLP_DIM if part == 'body' else cfg.MODEL.PyMAF.HF_MLP_DIM
sfeat_dim = body_sfeat_dim if part == 'body' else hf_sfeat_dim
for i in range(cfg.MODEL.PyMAF.N_ITER):
for f_i, f_dim in enumerate(filter_channels_default):
if sfeat_dim[i] > f_dim:
filter_start = f_i
break
filter_channels = [sfeat_dim[i]] + filter_channels_default[filter_start:]
if cfg.MODEL.PyMAF.GRID_ALIGN.USE_ATT and i >= self.att_starts:
self.maf_extractor[part].append(
MAF_Extractor(
filter_channels=filter_channels_default[att_feat_dim_idx:],
iwp_cam_mode=cfg.MODEL.USE_IWP_CAM
)
)
else:
self.maf_extractor[part].append(
MAF_Extractor(
filter_channels=filter_channels, iwp_cam_mode=cfg.MODEL.USE_IWP_CAM
)
)
self.part_module_names[part].update({f'maf_extractor.{part}': self.maf_extractor[part]})
# check all modules have been added to part_module_names
model_dict_all = dict.fromkeys(self.state_dict().keys())
for key in self.part_module_names.keys():
for name in list(model_dict_all.keys()):
for k in self.part_module_names[key].keys():
if name.startswith(k):
del model_dict_all[name]
# if name.startswith('regressor.') and '.smpl.' in name:
# del model_dict_all[name]
# if name.startswith('regressor.') and '.mano.' in name:
# del model_dict_all[name]
if name.startswith('regressor.') and '.init_' in name:
del model_dict_all[name]
if name == 'grid_points':
del model_dict_all[name]
assert (len(model_dict_all.keys()) == 0)
def init_mesh(self, batch_size, J_regressor=None, rw_cam={}):
""" initialize the mesh model with default poses and shapes
"""
if self.init_mesh_output is None or self.batch_size != batch_size:
self.init_mesh_output = self.regressor[0](
torch.zeros(batch_size), J_regressor=J_regressor, rw_cam=rw_cam, init_mode=True
)
self.batch_size = batch_size
return self.init_mesh_output
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(
self.inplanes,
planes * block.expansion,
kernel_size=1,
stride=stride,
bias=False
),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def _make_deconv_layer(self, num_layers, num_filters, num_kernels):
"""
Deconv_layer used in Simple Baselines:
Xiao et al. Simple Baselines for Human Pose Estimation and Tracking
https://github.com/microsoft/human-pose-estimation.pytorch
"""
assert num_layers == len(num_filters), \
'ERROR: num_deconv_layers is different len(num_deconv_filters)'
assert num_layers == len(num_kernels), \
'ERROR: num_deconv_layers is different len(num_deconv_filters)'
def _get_deconv_cfg(deconv_kernel, index):
if deconv_kernel == 4:
padding = 1
output_padding = 0
elif deconv_kernel == 3:
padding = 1
output_padding = 1
elif deconv_kernel == 2:
padding = 0
output_padding = 0
return deconv_kernel, padding, output_padding
layers = []
for i in range(num_layers):
kernel, padding, output_padding = _get_deconv_cfg(num_kernels[i], i)
planes = num_filters[i]
layers.append(
nn.ConvTranspose2d(
in_channels=self.inplanes,
out_channels=planes,
kernel_size=kernel,
stride=2,
padding=padding,
output_padding=output_padding,
bias=self.deconv_with_bias
)
)
layers.append(nn.BatchNorm2d(planes, momentum=BN_MOMENTUM))
layers.append(nn.ReLU(inplace=True))
self.inplanes = planes
return nn.Sequential(*layers)
def to(self, *args, **kwargs):
super().to(*args, **kwargs)
for m in ['body', 'hand', 'face']:
if m in self.smpl_family:
self.smpl_family[m].model.to(*args, **kwargs)
return self
def cuda(self, *args, **kwargs):
super().cuda(*args, **kwargs)
for m in ['body', 'hand', 'face']:
if m in self.smpl_family:
self.smpl_family[m].model.cuda(*args, **kwargs)
return self
def forward(self, batch={}, J_regressor=None, rw_cam={}):
'''
Args:
batch: input dictionary, including
images: 'img_{part}', for part in body, hand, and face if applicable
inversed affine transformation for the cropping of hand/face images: '{part}_theta_inv' for part in lhand, rhand, and face if applicable
J_regressor: joint regression matrix
rw_cam: real-world camera information, applied when cfg.MODEL.USE_IWP_CAM is False
Returns:
out_dict: the list containing the predicted parameters
vis_feat_list: the list containing features for visualization
'''
# batch keys: ['img_body', 'orig_height', 'orig_width', 'person_id', 'img_lhand',
# 'lhand_theta_inv', 'img_rhand', 'rhand_theta_inv', 'img_face', 'face_theta_inv']
# extract spatial features or global features
# run encoder for body
if 'body' in self.bhf_names:
img_body = batch['img_body']
batch_size = img_body.shape[0]
s_feat_body, g_feat = self.encoders['body'](batch['img_body'])
if cfg.MODEL.PyMAF.MAF_ON:
assert len(s_feat_body) == cfg.MODEL.PyMAF.N_ITER
# run encoders for hand / face
if 'hand' in self.bhf_names or 'face' in self.bhf_names:
limb_feat_dict = {}
limb_gfeat_dict = {}
if 'face' in self.bhf_names:
img_face = batch['img_face']
batch_size = img_face.shape[0]
limb_feat_dict['face'], limb_gfeat_dict['face'] = self.encoders['face'](img_face)
if 'hand' in self.bhf_names:
if 'lhand' in self.part_names:
img_rhand = batch['img_rhand']
batch_size = img_rhand.shape[0]
# flip left hand images
img_lhand = torch.flip(batch['img_lhand'], [3])
img_hands = torch.cat([img_rhand, img_lhand])
s_feat_hands, g_feat_hands = self.encoders['hand'](img_hands)
limb_feat_dict['rhand'] = [feat[:batch_size] for feat in s_feat_hands]
limb_feat_dict['lhand'] = [feat[batch_size:] for feat in s_feat_hands]
if g_feat_hands is not None:
limb_gfeat_dict['rhand'] = g_feat_hands[:batch_size]
limb_gfeat_dict['lhand'] = g_feat_hands[batch_size:]
else:
img_rhand = batch['img_rhand']
batch_size = img_rhand.shape[0]
limb_feat_dict['rhand'], limb_gfeat_dict['rhand'] = self.encoders['hand'](
img_rhand
)
if cfg.MODEL.PyMAF.MAF_ON:
for k in limb_feat_dict.keys():
assert len(limb_feat_dict[k]) == cfg.MODEL.PyMAF.N_ITER
out_dict = {}
# grid-pattern points
grid_points = torch.transpose(self.grid_points.expand(batch_size, -1, -1), 1, 2)
# initial parameters
mesh_output = self.init_mesh(batch_size, J_regressor, rw_cam)
out_dict['mesh_out'] = [mesh_output]
out_dict['dp_out'] = []
# for visulization
vis_feat_list = []
# dense prediction during training
if not cfg.MODEL.EVAL_MODE:
if 'body' in self.bhf_names:
if cfg.MODEL.PyMAF.AUX_SUPV_ON:
iuv_out_dict = self.dp_head(s_feat_body[-1])
out_dict['dp_out'].append(iuv_out_dict)
elif self.hand_only_mode:
if cfg.MODEL.PyMAF.HF_AUX_SUPV_ON:
out_dict['rhand_dpout'] = []
dphand_out_dict = self.dp_head_hf['hand'](limb_feat_dict['rhand'][-1])
out_dict['rhand_dpout'].append(dphand_out_dict)
elif self.face_only_mode:
if cfg.MODEL.PyMAF.HF_AUX_SUPV_ON:
out_dict['face_dpout'] = []
dpface_out_dict = self.dp_head_hf['face'](limb_feat_dict['face'][-1])
out_dict['face_dpout'].append(dpface_out_dict)
# parameter predictions
for rf_i in range(cfg.MODEL.PyMAF.N_ITER):
current_states = {}
if 'body' in self.bhf_names:
pred_cam = mesh_output['pred_cam'].detach()
pred_shape = mesh_output['pred_shape'].detach()
pred_pose = mesh_output['pred_pose'].detach()
current_states['init_cam'] = pred_cam
current_states['init_shape'] = pred_shape
current_states['init_pose'] = pred_pose
pred_smpl_verts = mesh_output['verts'].detach()
if cfg.MODEL.PyMAF.MAF_ON:
s_feat_i = s_feat_body[rf_i]
# re-project mesh on the image plane
if self.hand_only_mode:
pred_cam = mesh_output['pred_cam'].detach()
pred_rhand_v = self.mano_sampler(mesh_output['verts_rh'])
pred_rhand_proj = projection(
pred_rhand_v, {**rw_cam, 'cam_sxy': pred_cam}, iwp_mode=cfg.MODEL.USE_IWP_CAM
)
if cfg.MODEL.USE_IWP_CAM:
pred_rhand_proj = pred_rhand_proj / (224. / 2.)
else:
pred_rhand_proj = j2d_processing(pred_rhand_proj, rw_cam['kps_transf'])
proj_hf_center = {
'rhand': mesh_output['pred_rhand_kp2d'][:,
self.hf_root_idx['rhand']].unsqueeze(1)
}
proj_hf_pts = {
'rhand': torch.cat([proj_hf_center['rhand'], pred_rhand_proj], dim=1)
}
elif self.face_only_mode:
pred_cam = mesh_output['pred_cam'].detach()
pred_face_v = mesh_output['pred_face_kp3d']
pred_face_proj = projection(
pred_face_v, {**rw_cam, 'cam_sxy': pred_cam}, iwp_mode=cfg.MODEL.USE_IWP_CAM
)
if cfg.MODEL.USE_IWP_CAM:
pred_face_proj = pred_face_proj / (224. / 2.)
else:
pred_face_proj = j2d_processing(pred_face_proj, rw_cam['kps_transf'])
proj_hf_center = {
'face': mesh_output['pred_face_kp2d'][:, self.hf_root_idx['face']].unsqueeze(1)
}
proj_hf_pts = {'face': torch.cat([proj_hf_center['face'], pred_face_proj], dim=1)}
elif self.body_hand_mode:
pred_lhand_v = self.mano_sampler(pred_smpl_verts[:, self.smpl2lhand])
pred_rhand_v = self.mano_sampler(pred_smpl_verts[:, self.smpl2rhand])
pred_hand_v = torch.cat([pred_lhand_v, pred_rhand_v], dim=1)
pred_hand_proj = projection(
pred_hand_v, {**rw_cam, 'cam_sxy': pred_cam}, iwp_mode=cfg.MODEL.USE_IWP_CAM
)
if cfg.MODEL.USE_IWP_CAM:
pred_hand_proj = pred_hand_proj / (224. / 2.)
else:
pred_hand_proj = j2d_processing(pred_hand_proj, rw_cam['kps_transf'])
proj_hf_center = {
'lhand': mesh_output['pred_lhand_kp2d'][:,
self.hf_root_idx['lhand']].unsqueeze(1),
'rhand': mesh_output['pred_rhand_kp2d'][:,
self.hf_root_idx['rhand']].unsqueeze(1),
}
proj_hf_pts = {
'lhand':
torch.cat([proj_hf_center['lhand'], pred_hand_proj[:, :self.mano_ds_len]],
dim=1),
'rhand':
torch.cat([proj_hf_center['rhand'], pred_hand_proj[:, self.mano_ds_len:]],
dim=1),
}
elif self.full_body_mode:
pred_lhand_v = self.mano_sampler(pred_smpl_verts[:, self.smpl2lhand])
pred_rhand_v = self.mano_sampler(pred_smpl_verts[:, self.smpl2rhand])
pred_hand_v = torch.cat([pred_lhand_v, pred_rhand_v], dim=1)
pred_hand_proj = projection(
pred_hand_v, {**rw_cam, 'cam_sxy': pred_cam}, iwp_mode=cfg.MODEL.USE_IWP_CAM
)
if cfg.MODEL.USE_IWP_CAM:
pred_hand_proj = pred_hand_proj / (224. / 2.)
else:
pred_hand_proj = j2d_processing(pred_hand_proj, rw_cam['kps_transf'])
proj_hf_center = {
'lhand': mesh_output['pred_lhand_kp2d'][:,
self.hf_root_idx['lhand']].unsqueeze(1),
'rhand': mesh_output['pred_rhand_kp2d'][:,
self.hf_root_idx['rhand']].unsqueeze(1),
'face': mesh_output['pred_face_kp2d'][:, self.hf_root_idx['face']].unsqueeze(1)
}
proj_hf_pts = {
'lhand':
torch.cat([proj_hf_center['lhand'], pred_hand_proj[:, :self.mano_ds_len]],
dim=1), 'rhand':
torch.cat([proj_hf_center['rhand'], pred_hand_proj[:, self.mano_ds_len:]],
dim=1), 'face':
torch.cat([proj_hf_center['face'], mesh_output['pred_face_kp2d']], dim=1)
}
# extract mesh-aligned features for the hand / face part
if 'hand' in self.bhf_names or 'face' in self.bhf_names:
limb_rf_i = rf_i
hand_face_feat = {}
for hf_i, part_name in enumerate(self.part_names):
if 'hand' in part_name:
hf_key = 'hand'
elif 'face' in part_name:
hf_key = 'face'
if cfg.MODEL.PyMAF.MAF_ON:
if cfg.MODEL.PyMAF.HF_BACKBONE == 'res50':
limb_feat_i = limb_feat_dict[part_name][limb_rf_i]
else:
raise NotImplementedError
limb_reduce_dim = (not self.fuse_grid_align) or (rf_i < self.att_starts)
if limb_rf_i == 0 or cfg.MODEL.PyMAF.GRID_FEAT:
limb_ref_feat_ctd = self.maf_extractor[hf_key][limb_rf_i].sampling(
grid_points, im_feat=limb_feat_i, reduce_dim=limb_reduce_dim
)
else:
if self.hand_only_mode or self.face_only_mode:
proj_hf_pts_crop = proj_hf_pts[part_name][:, :, :2]
proj_hf_v_center = proj_hf_pts_crop[:, 0].unsqueeze(1)
if cfg.MODEL.PyMAF.HF_BOX_CENTER:
part_box_ul = torch.min(proj_hf_pts_crop, dim=1)[0].unsqueeze(1)
part_box_br = torch.max(proj_hf_pts_crop, dim=1)[0].unsqueeze(1)
part_box_center = (part_box_ul + part_box_br) / 2.
proj_hf_pts_crop_ctd = proj_hf_pts_crop[:, 1:] - part_box_center
else:
proj_hf_pts_crop_ctd = proj_hf_pts_crop[:, 1:]
elif self.full_body_mode or self.body_hand_mode:
# convert projection points to the space of cropped hand/face images
theta_i_inv = batch[f'{part_name}_theta_inv']
proj_hf_pts_crop = torch.bmm(
theta_i_inv,
homo_vector(proj_hf_pts[part_name][:, :, :2]).permute(0, 2, 1)
).permute(0, 2, 1)
if part_name == 'lhand':
flip_x = torch.tensor([-1, 1])[None,
None, :].to(proj_hf_pts_crop)
proj_hf_pts_crop *= flip_x
if cfg.MODEL.PyMAF.HF_BOX_CENTER:
# align projection points with the cropped image center
part_box_ul = torch.min(proj_hf_pts_crop, dim=1)[0].unsqueeze(1)
part_box_br = torch.max(proj_hf_pts_crop, dim=1)[0].unsqueeze(1)
part_box_center = (part_box_ul + part_box_br) / 2.
proj_hf_pts_crop_ctd = proj_hf_pts_crop[:, 1:] - part_box_center
else:
proj_hf_pts_crop_ctd = proj_hf_pts_crop[:, 1:]
# 0 is the root point
proj_hf_v_center = proj_hf_pts_crop[:, 0].unsqueeze(1)
limb_ref_feat_ctd = self.maf_extractor[hf_key][limb_rf_i].sampling(
proj_hf_pts_crop_ctd.detach(),
im_feat=limb_feat_i,
reduce_dim=limb_reduce_dim
)
if self.fuse_grid_align and limb_rf_i >= self.att_starts:
limb_grid_feature_ctd = self.maf_extractor[hf_key][limb_rf_i].sampling(
grid_points, im_feat=limb_feat_i, reduce_dim=limb_reduce_dim
)
limb_grid_ref_feat_ctd = torch.cat([
limb_grid_feature_ctd, limb_ref_feat_ctd
],
dim=-1).permute(0, 2, 1)
if cfg.MODEL.PyMAF.GRID_ALIGN.USE_ATT:
att_ref_feat_ctd = self.align_attention[hf_key][
limb_rf_i - self.att_starts](limb_grid_ref_feat_ctd)[0]
elif cfg.MODEL.PyMAF.GRID_ALIGN.USE_FC:
att_ref_feat_ctd = limb_grid_ref_feat_ctd
att_ref_feat_ctd = self.maf_extractor[hf_key][limb_rf_i].reduce_dim(
att_ref_feat_ctd.permute(0, 2, 1)
).view(batch_size, -1)
limb_ref_feat_ctd = self.att_feat_reduce[hf_key][
limb_rf_i - self.att_starts](att_ref_feat_ctd)
else:
# limb_ref_feat = limb_ref_feat.view(batch_size, -1)
limb_ref_feat_ctd = limb_ref_feat_ctd.view(batch_size, -1)
hand_face_feat[part_name] = limb_ref_feat_ctd
else:
hand_face_feat[part_name] = limb_gfeat_dict[part_name]
# extract mesh-aligned features for the body part
if 'body' in self.bhf_names:
if cfg.MODEL.PyMAF.MAF_ON:
reduce_dim = (not self.fuse_grid_align) or (rf_i < self.att_starts)
if rf_i == 0 or cfg.MODEL.PyMAF.GRID_FEAT:
ref_feature = self.maf_extractor['body'][rf_i].sampling(
grid_points, im_feat=s_feat_i, reduce_dim=reduce_dim
)
else:
# TODO: use a more sparse SMPL implementation (with 431 vertices) for acceleration
pred_smpl_verts_ds = self.mesh_sampler.downsample(
pred_smpl_verts
) # [B, 431, 3]
ref_feature = self.maf_extractor['body'][rf_i](
pred_smpl_verts_ds,
im_feat=s_feat_i,
cam={**rw_cam, 'cam_sxy': pred_cam},
add_att=True,
reduce_dim=reduce_dim
) # [B, 431 * n_feat]
if self.fuse_grid_align and rf_i >= self.att_starts:
if rf_i > 0 and not cfg.MODEL.PyMAF.GRID_FEAT:
grid_feature = self.maf_extractor['body'][rf_i].sampling(
grid_points, im_feat=s_feat_i, reduce_dim=reduce_dim
)
grid_ref_feat = torch.cat([grid_feature, ref_feature], dim=-1)
else:
grid_ref_feat = ref_feature
grid_ref_feat = grid_ref_feat.permute(0, 2, 1)
if cfg.MODEL.PyMAF.GRID_ALIGN.USE_ATT:
att_ref_feat = self.align_attention['body'][
rf_i - self.att_starts](grid_ref_feat)[0]
elif cfg.MODEL.PyMAF.GRID_ALIGN.USE_FC:
att_ref_feat = grid_ref_feat
att_ref_feat = self.maf_extractor['body'][rf_i].reduce_dim(
att_ref_feat.permute(0, 2, 1)
)
att_ref_feat = att_ref_feat.view(batch_size, -1)
ref_feature = self.att_feat_reduce['body'][rf_i -
self.att_starts](att_ref_feat)
else:
ref_feature = ref_feature.view(batch_size, -1)
else:
ref_feature = g_feat
else:
ref_feature = None
if not self.smpl_mode:
if self.hand_only_mode:
current_states['xc_rhand'] = hand_face_feat['rhand']
elif self.face_only_mode:
current_states['xc_face'] = hand_face_feat['face']
elif self.body_hand_mode:
current_states['xc_lhand'] = hand_face_feat['lhand']
current_states['xc_rhand'] = hand_face_feat['rhand']
elif self.full_body_mode:
current_states['xc_lhand'] = hand_face_feat['lhand']
current_states['xc_rhand'] = hand_face_feat['rhand']
current_states['xc_face'] = hand_face_feat['face']
if rf_i > 0:
for part in self.part_names:
current_states[f'init_{part}'] = mesh_output[f'pred_{part}'].detach()
if part == 'face':
current_states['init_exp'] = mesh_output['pred_exp'].detach()
if self.hand_only_mode:
current_states['init_shape_rh'] = mesh_output['pred_shape_rh'].detach()
current_states['init_orient_rh'] = mesh_output['pred_orient_rh'].detach()
current_states['init_cam_rh'] = mesh_output['pred_cam_rh'].detach()
elif self.face_only_mode:
current_states['init_shape_fa'] = mesh_output['pred_shape_fa'].detach()
current_states['init_orient_fa'] = mesh_output['pred_orient_fa'].detach()
current_states['init_cam_fa'] = mesh_output['pred_cam_fa'].detach()
elif self.full_body_mode or self.body_hand_mode:
if cfg.MODEL.PyMAF.OPT_WRIST:
current_states['init_shape_lh'] = mesh_output['pred_shape_lh'].detach()
current_states['init_orient_lh'] = mesh_output['pred_orient_lh'].detach(
)
current_states['init_cam_lh'] = mesh_output['pred_cam_lh'].detach()
current_states['init_shape_rh'] = mesh_output['pred_shape_rh'].detach()
current_states['init_orient_rh'] = mesh_output['pred_orient_rh'].detach(
)
current_states['init_cam_rh'] = mesh_output['pred_cam_rh'].detach()
# update mesh parameters
mesh_output = self.regressor[rf_i](
ref_feature,
n_iter=1,
J_regressor=J_regressor,
rw_cam=rw_cam,
global_iter=rf_i,
**current_states
)
out_dict['mesh_out'].append(mesh_output)
return out_dict, vis_feat_list
def pymaf_net(smpl_mean_params, pretrained=True, device=torch.device('cuda')):
""" Constructs an PyMAF model with ResNet50 backbone.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = PyMAF(smpl_mean_params, pretrained, device)
return model
|