from __future__ import absolute_import from __future__ import print_function from __future__ import division import torch IMG_FEAT_DIM = { 'resnet': 2048, 'vit': 1024 } N_JOINTS = 17 root = 'dataset' class PATHS: # Raw data folders PARSED_DATA = f'{root}/parsed_data' AMASS_PTH = f'{root}/AMASS' THREEDPW_PTH = f'{root}/3DPW' HUMAN36M_PTH = f'{root}/Human36M' RICH_PTH = f'{root}/RICH' EMDB_PTH = f'{root}/EMDB' # Processed labels AMASS_LABEL = f'{root}/parsed_data/amass.pth' THREEDPW_LABEL = f'{root}/parsed_data/3dpw_dset_backbone.pth' MPII3D_LABEL = f'{root}/parsed_data/mpii3d_dset_backbone.pth' HUMAN36M_LABEL = f'{root}/parsed_data/human36m_dset_backbone.pth' INSTA_LABEL = f'{root}/parsed_data/insta_dset_backbone.pth' BEDLAM_LABEL = f'{root}/parsed_data/bedlam_train_backbone.pth' class KEYPOINTS: NUM_JOINTS = N_JOINTS H36M_TO_J17 = [6, 5, 4, 1, 2, 3, 16, 15, 14, 11, 12, 13, 8, 10, 0, 7, 9] H36M_TO_J14 = H36M_TO_J17[:14] J17_TO_H36M = [14, 3, 4, 5, 2, 1, 0, 15, 12, 16, 13, 9, 10, 11, 8, 7, 6] COCO_AUG_DICT = f'{root}/body_models/coco_aug_dict.pth' TREE = [[5, 6], 0, 0, 1, 2, -1, -1, 5, 6, 7, 8, -1, -1, 11, 12, 13, 14, 15, 15, 15, 16, 16, 16] # STD scale for video noise S_BIAS = 1e-1 S_JITTERING = 5e-2 S_PEAK = 3e-1 S_PEAK_MASK = 5e-3 S_MASK = 0.03 class BMODEL: MAIN_JOINTS = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21] # reduced_joints FLDR = f'{root}/body_models/smpl/' SMPLX2SMPL = f'{root}/body_models/smplx2smpl.pkl' FACES = f'{root}/body_models/smpl_faces.npy' MEAN_PARAMS = f'{root}/body_models/smpl_mean_params.npz' JOINTS_REGRESSOR_WHAM = f'{root}/body_models/J_regressor_wham.npy' JOINTS_REGRESSOR_H36M = f'{root}/body_models/J_regressor_h36m.npy' JOINTS_REGRESSOR_EXTRA = f'{root}/body_models/J_regressor_extra.npy' JOINTS_REGRESSOR_FEET = f'{root}/body_models/J_regressor_feet.npy' PARENTS = torch.tensor([ -1, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 9, 9, 12, 13, 14, 16, 17, 18, 19, 20, 21])