add other models

10_FLAME2020/FLAME_albedo_from_BFM.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c6a9986024679caa881fcfe8a49687843e9338bfe0b0bb4923dccd29e01135ad
+size 1258291694

10_FLAME2020/FLAME_masks.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ccefbe1ac0774ff78c68caf2c627b4abc067a6555ebeb0be5d5b0812366ab492
+size 215062

10_FLAME2020/FLAME_masks/FLAME_masks.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ccefbe1ac0774ff78c68caf2c627b4abc067a6555ebeb0be5d5b0812366ab492
+size 215062

10_FLAME2020/FLAME_masks/readme

@@ -0,0 +1,2 @@
+Dictionary with vertex indices for different masks for the publicly available FLAME head model (https://flame.is.tue.mpg.de/).
+See the gif for a visualization of all masks.

10_FLAME2020/SMPL-X__FLAME_vertex_ids.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7e70cdc3659aae699b9732e8dd4af49106310c69b90dc83d9f73e96dbf871e49
+size 40312

10_FLAME2020/female_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:efee38e24c9f9f7cc01120a5a48201a787e97e6e20af455747fd60f3dc06eb68
+size 53023716

10_FLAME2020/flame2020to2019_exp_trafo.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d449716bb73e85ba38eadcaa1cb3f97e3af38b6b8411bcec4886e3bd69ba0bc6
+size 80128

10_FLAME2020/flame_dynamic_embedding.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fd046a4740f5e6837e65622d0e050273bb71bbcb1ad6ab322474013a2a23de37
+size 42639

10_FLAME2020/flame_static_embedding_68_v4.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:19c1a63594100c9481a917dd95424f3a66119ea31c153f0a318fb38dc5c2f2e6
+size 2398

10_FLAME2020/generic_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:efcd14cc4a69f3a3d9af8ded80146b5b6b50df3bd74cf69108213b144eba725b
+size 53023716

10_FLAME2020/l_eyelid.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fa50997166c2f884fbfc577d99fe8707966763fc3d83a1870bb786df6cc9410d
+size 120680

10_FLAME2020/landmark_embedding.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8095348eeafce5a02f6bd8765146307f9567a3f03b316d788a2e47336d667954
+size 31292

10_FLAME2020/male_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5a996eaca4b56b03a0c2907f7dfa3a92680d965fd190d345a7e588f6fa017d1b
+size 53023716

10_FLAME2020/mediapipe_landmark_embedding__smplx.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ebc5f1e95cb52cfca1549b58dbfd397863d3af3aac5056c886b0c6121da0ae2a
+size 4518

10_FLAME2020/r_eyelid.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dd78776fed765be4468889dd7d5b54b4bf61c8efbb2869b5b91409ce798aad8d
+size 120680

11_PIXIE/MANO_SMPLX_vertex_ids.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e5abe70b6574de25470475091e8008314a5b90127eb48c3e63bfa0adf8c04dcf
+size 13535

11_PIXIE/SMPL-X__FLAME_vertex_ids.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7e70cdc3659aae699b9732e8dd4af49106310c69b90dc83d9f73e96dbf871e49
+size 40312

11_PIXIE/SMPLX_NEUTRAL_2020.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bdf06146e27d92022fe5dadad3b9203373f6879eca8e4d8235359ee3ec6a5a74
+size 167264530

11_PIXIE/SMPLX_to_J14.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5df844ddea85b0a400a2e8dbe63d09d19f2b1b7ec0e0e952daeae08f83d82d61
+size 4692193

11_PIXIE/flame2smplx_tex_1024.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:945d164becfe0f217e1a51356e843ef3f615278d66f6bbb9a7a8f70cb356c93b
+size 22043098

11_PIXIE/pixie_model.tar

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9dade173cbe63527209bfd413b9698e8f194ac9bfb9cf11012a823b4c75ed6b9
+size 773605748

11_PIXIE/smplx_extra_joints.yaml

@@ -0,0 +1,132 @@
+head_top:
+  bc:
+  - 0.8277337276382795
+  - 0.1422200962169292
+  - 0.030046176144791284
+  face: 2581
+left_big_toe:
+  bc:
+  - 0.0
+  - 0.0
+  - 1.0
+  face: 4407
+left_ear:
+  bc:
+  - 0.0
+  - 0.0
+  - 1.0
+  face: 1946
+left_eye:
+  bc:
+  - 0.0
+  - 1.0
+  - 0.0
+  face: 9470
+left_heel:
+  bc:
+  - 1.0
+  - 0.0
+  - 0.0
+  face: 4621
+left_index:
+  bc:
+  - 0.0
+  - 0.0
+  - 1.0
+  face: 3720
+left_middle:
+  bc:
+  - 0.0
+  - 0.0
+  - 1.0
+  face: 3469
+left_pinky:
+  bc:
+  - 1.0
+  - 0.0
+  - 0.0
+  face: 3575
+left_ring:
+  bc:
+  - 0.0
+  - 0.0
+  - 1.0
+  face: 3542
+left_small_toe:
+  bc:
+  - 1.0
+  - 0.0
+  - 0.0
+  face: 4329
+left_thumb:
+  bc:
+  - 0.0
+  - 1.0
+  - 0.0
+  face: 3630
+nose:
+  bc:
+  - 0.0
+  - 1.0
+  - 0.0
+  face: 9041
+right_big_toe:
+  bc:
+  - 1.0
+  - 0.0
+  - 0.0
+  face: 8094
+right_ear:
+  bc:
+  - 1.0
+  - 0.0
+  - 0.0
+  face: 351
+right_eye:
+  bc:
+  - 1.0
+  - 0.0
+  - 0.0
+  face: 10093
+right_heel:
+  bc:
+  - 1.0
+  - 0.0
+  - 0.0
+  face: 8247
+right_index:
+  bc:
+  - 1.0
+  - 0.0
+  - 0.0
+  face: 6919
+right_middle:
+  bc:
+  - 0.0
+  - 0.0
+  - 1.0
+  face: 7050
+right_pinky:
+  bc:
+  - 1.0
+  - 0.0
+  - 0.0
+  face: 7284
+right_ring:
+  bc:
+  - 0.0
+  - 0.0
+  - 1.0
+  face: 7168
+right_small_toe:
+  bc:
+  - 0.0
+  - 0.0
+  - 1.0
+  face: 8096
+right_thumb:
+  bc:
+  - 0.0
+  - 0.0
+  - 1.0
+  face: 7370

12_vposer_v1_0/TR00_004_00_WO_accad.ini

@@ -0,0 +1,29 @@
+[All]
+adam_beta1 : 0.9
+base_lr : 0.005
+batch_size : 512
+best_model_fname : None
+cuda_id : 0
+data_shape : [1, 21, 3]
+dataset_dir : None
+display_model_gender : male
+expr_code : 004_00_WO_accad
+fp_precision : 32
+ip_avoid : False
+kl_coef : 0.005
+latentD : 32
+log_every_epoch : 2
+model_type : smpl
+n_workers : 10
+num_bodies_to_display : 10
+num_epochs : 100
+num_neurons : 512
+reg_coef : 0.0001
+remove_Zrot : True
+seed : 4815
+sm_coef : 0.01
+test_only : False
+try_num : 0
+use_cont_repr : True
+verbosity : 0
+work_dir : None

12_vposer_v1_0/snapshots/._TR00_E096.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4e2615cd1d2e78cdfac7169c6182a7352d02992336dad7329d3d97f6947fb515
+size 4096

12_vposer_v1_0/snapshots/TR00_E096.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0e4ad40f922606989939d3fae6eadf82d1a8e98112dffb6e39d89d6471270d5c
+size 2702962

12_vposer_v1_0/version.txt

@@ -0,0 +1 @@
+The codenames "TR00_004_00_WO_accad" and "TR00_E096" correspond to "VPoser Version 1.0".

12_vposer_v1_0/vposer_smpl.py

@@ -0,0 +1,164 @@
+# -*- coding: utf-8 -*-
+#
+# Copyright (C) 2019 Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG),
+# acting on behalf of its Max Planck Institute for Intelligent Systems and the
+# Max Planck Institute for Biological Cybernetics. All rights reserved.
+#
+# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is holder of all proprietary rights
+# on this computer program. You can only use this computer program if you have closed a license agreement
+# with MPG or you get the right to use the computer program from someone who is authorized to grant you that right.
+# Any use of the computer program without a valid license is prohibited and liable to prosecution.
+# Contact: [email protected]
+#
+#
+# If you use this code in a research publication please consider citing the following:
+#
+# Expressive Body Capture: 3D Hands, Face, and Body from a Single Image <https://arxiv.org/abs/1904.05866>
+# AMASS: Archive of Motion Capture as Surface Shapes <https://arxiv.org/abs/1904.03278>
+#
+#
+# Code Developed by:
+# Nima Ghorbani <https://www.linkedin.com/in/nghorbani/>
+# Vassilis Choutas <https://ps.is.tuebingen.mpg.de/employees/vchoutas> for ContinousRotReprDecoder
+#
+# 2018.01.02
+
+'''
+A human body pose prior built with Auto-Encoding Variational Bayes
+'''
+
+__all__ = ['VPoser']
+
+import os, sys, shutil
+
+import torch
+
+from torch import nn
+from torch.nn import functional as F
+
+import numpy as np
+
+import torchgeometry as tgm
+
+class ContinousRotReprDecoder(nn.Module):
+    def __init__(self):
+        super(ContinousRotReprDecoder, self).__init__()
+
+    def forward(self, module_input):
+        reshaped_input = module_input.view(-1, 3, 2)
+
+        b1 = F.normalize(reshaped_input[:, :, 0], dim=1)
+
+        dot_prod = torch.sum(b1 * reshaped_input[:, :, 1], dim=1, keepdim=True)
+        b2 = F.normalize(reshaped_input[:, :, 1] - dot_prod * b1, dim=-1)
+        b3 = torch.cross(b1, b2, dim=1)
+
+        return torch.stack([b1, b2, b3], dim=-1)
+
+
+class VPoser(nn.Module):
+    def __init__(self, num_neurons, latentD, data_shape, use_cont_repr=True):
+        super(VPoser, self).__init__()
+
+        self.latentD = latentD
+        self.use_cont_repr = use_cont_repr
+
+        n_features = np.prod(data_shape)
+        self.num_joints = data_shape[1]
+
+        self.bodyprior_enc_bn1 = nn.BatchNorm1d(n_features)
+        self.bodyprior_enc_fc1 = nn.Linear(n_features, num_neurons)
+        self.bodyprior_enc_bn2 = nn.BatchNorm1d(num_neurons)
+        self.bodyprior_enc_fc2 = nn.Linear(num_neurons, num_neurons)
+        self.bodyprior_enc_mu = nn.Linear(num_neurons, latentD)
+        self.bodyprior_enc_logvar = nn.Linear(num_neurons, latentD)
+        self.dropout = nn.Dropout(p=.1, inplace=False)
+
+        self.bodyprior_dec_fc1 = nn.Linear(latentD, num_neurons)
+        self.bodyprior_dec_fc2 = nn.Linear(num_neurons, num_neurons)
+
+        if self.use_cont_repr:
+            self.rot_decoder = ContinousRotReprDecoder()
+
+        self.bodyprior_dec_out = nn.Linear(num_neurons, self.num_joints* 6)
+
+    def encode(self, Pin):
+        '''
+
+        :param Pin: Nx(numjoints*3)
+        :param rep_type: 'matrot'/'aa' for matrix rotations or axis-angle
+        :return:
+        '''
+        Xout = Pin.view(Pin.size(0), -1)  # flatten input
+        Xout = self.bodyprior_enc_bn1(Xout)
+
+        Xout = F.leaky_relu(self.bodyprior_enc_fc1(Xout), negative_slope=.2)
+        Xout = self.bodyprior_enc_bn2(Xout)
+        Xout = self.dropout(Xout)
+        Xout = F.leaky_relu(self.bodyprior_enc_fc2(Xout), negative_slope=.2)
+        return torch.distributions.normal.Normal(self.bodyprior_enc_mu(Xout), F.softplus(self.bodyprior_enc_logvar(Xout)))
+
+    def decode(self, Zin, output_type='matrot'):
+        assert output_type in ['matrot', 'aa']
+
+        Xout = F.leaky_relu(self.bodyprior_dec_fc1(Zin), negative_slope=.2)
+        Xout = self.dropout(Xout)
+        Xout = F.leaky_relu(self.bodyprior_dec_fc2(Xout), negative_slope=.2)
+        Xout = self.bodyprior_dec_out(Xout)
+        if self.use_cont_repr:
+            Xout = self.rot_decoder(Xout)
+        else:
+            Xout = torch.tanh(Xout)
+
+        Xout = Xout.view([-1, 1, self.num_joints, 9])
+        if output_type == 'aa': return VPoser.matrot2aa(Xout)
+        return Xout
+
+    def forward(self, Pin, input_type='matrot', output_type='matrot'):
+        '''
+
+        :param Pin: aa: Nx1xnum_jointsx3 / matrot: Nx1xnum_jointsx9
+        :param input_type: matrot / aa for matrix rotations or axis angles
+        :param output_type: matrot / aa
+        :return:
+        '''
+        assert output_type in ['matrot', 'aa']
+        # if input_type == 'aa': Pin = VPoser.aa2matrot(Pin)
+        q_z = self.encode(Pin)
+        q_z_sample = q_z.rsample()
+        Prec = self.decode(q_z_sample)
+        if output_type == 'aa': Prec = VPoser.matrot2aa(Prec)
+
+        #return Prec, q_z.mean, q_z.sigma
+        return {'pose':Prec, 'mean':q_z.mean, 'std':q_z.scale}
+
+    def sample_poses(self, num_poses, output_type='aa', seed=None):
+        np.random.seed(seed)
+        dtype = self.bodyprior_dec_fc1.weight.dtype
+        device = self.bodyprior_dec_fc1.weight.device
+        self.eval()
+        with torch.no_grad():
+            Zgen = torch.tensor(np.random.normal(0., 1., size=(num_poses, self.latentD)), dtype=dtype).to(device)
+        return self.decode(Zgen, output_type=output_type)
+
+    @staticmethod
+    def matrot2aa(pose_matrot):
+        '''
+        :param pose_matrot: Nx1xnum_jointsx9
+        :return: Nx1xnum_jointsx3
+        '''
+        batch_size = pose_matrot.size(0)
+        homogen_matrot = F.pad(pose_matrot.view(-1, 3, 3), [0,1])
+        pose = tgm.rotation_matrix_to_angle_axis(homogen_matrot).view(batch_size, 1, -1, 3).contiguous()
+        return pose
+
+    @staticmethod
+    def aa2matrot(pose):
+        '''
+        :param Nx1xnum_jointsx3
+        :return: pose_matrot: Nx1xnum_jointsx9
+        '''
+        batch_size = pose.size(0)
+        pose_body_matrot = tgm.angle_axis_to_rotation_matrix(pose.reshape(-1, 3))[:, :3, :3].contiguous().view(batch_size, 1, -1, 9)
+        return pose_body_matrot
+

13_vposer_V02_05/V02_05.log

@@ -0,0 +1,80 @@
+[V02_05] -- VPoser dataset already exists at /ps/scratch/human_body_prior/VPoser/data/smpl/V02_03
+[V02_05] -- Total Trainable Parameters Count in vp_model is 0.94 M.
+[V02_05] -- Epoch 0: val_loss:0.51
+[V02_05] -- lr is [0.001]
+[V02_05] -- Created a git archive backup at /ps/scratch/human_body_prior/VPoser/training_experiments/V02_05/code/vposer_2020_10_23_17_10_39.tar.gz
+[V02_05] -- Epoch 0: val_loss:0.03
+[V02_05] -- lr is [0.001]
+[V02_05] -- Epoch 1: val_loss:0.03
+[V02_05] -- lr is [0.001]
+[V02_05] -- Epoch 2: val_loss:0.03
+[V02_05] -- lr is [0.001]
+[V02_05] -- Epoch 3: val_loss:0.03
+[V02_05] -- lr is [0.001]
+[V02_05] -- Epoch 4: val_loss:0.03
+[V02_05] -- lr is [0.001]
+[V02_05] -- Epoch 5: val_loss:0.03
+[V02_05] -- lr is [0.001]
+[V02_05] -- Epoch 6: val_loss:0.03
+[V02_05] -- lr is [0.001]
+[V02_05] -- Epoch 7: val_loss:0.03
+[V02_05] -- lr is [0.001]
+[V02_05] -- Epoch 8: val_loss:0.03
+[V02_05] -- lr is [0.001]
+[V02_05] -- Epoch 9: val_loss:0.03
+[V02_05] -- lr is [0.001]
+[V02_05] -- Epoch 10: val_loss:0.03
+[V02_05] -- lr is [0.001]
+[V02_05] -- Epoch 11: val_loss:0.03
+[V02_05] -- lr is [0.001]
+[V02_05] -- Epoch 12: val_loss:0.03
+[V02_05] -- lr is [0.001]
+[V02_05] -- Epoch 13: val_loss:0.03
+[V02_05] -- lr is [0.001]
+[V02_05] -- Epoch 14: val_loss:0.03
+[V02_05] -- lr is [0.001]
+[V02_05] -- Epoch 15: val_loss:0.04
+[V02_05] -- lr is [0.0001]
+[V02_05] -- Epoch 16: val_loss:0.04
+[V02_05] -- lr is [0.0001]
+[V02_05] -- Epoch 17: val_loss:0.04
+[V02_05] -- lr is [0.0001]
+[V02_05] -- Epoch 18: val_loss:0.04
+[V02_05] -- lr is [0.0001]
+[V02_05] -- VPoser dataset already exists at /ps/scratch/human_body_prior/VPoser/data/smpl/V02_03
+[V02_05] -- Total Trainable Parameters Count in vp_model is 0.94 M.
+[V02_05] -- Epoch 0: val_loss:0.03
+[V02_05] -- lr is [0.001]
+[V02_05] -- Created a git archive backup at /ps/scratch/human_body_prior/VPoser/training_experiments/V02_05/code/vposer_2020_10_23_18_48_23.tar.gz
+[V02_05] -- Epoch 9: val_loss:0.03
+[V02_05] -- lr is [0.001]
+[V02_05] -- Epoch 10: val_loss:0.03
+[V02_05] -- lr is [0.001]
+[V02_05] -- Epoch 11: val_loss:0.03
+[V02_05] -- lr is [0.001]
+[V02_05] -- Epoch 12: val_loss:0.03
+[V02_05] -- lr is [0.001]
+[V02_05] -- Epoch 13: val_loss:0.03
+[V02_05] -- lr is [0.0001]
+[V02_05] -- Epoch 14: val_loss:0.03
+[V02_05] -- lr is [0.0001]
+[V02_05] -- Epoch 15: val_loss:0.04
+[V02_05] -- lr is [0.0001]
+[V02_05] -- Epoch 16: val_loss:0.04
+[V02_05] -- lr is [0.0001]
+[V02_05] -- Epoch 17: val_loss:0.04
+[V02_05] -- lr is [0.0001]
+[V02_05] -- Epoch 18: val_loss:0.04
+[V02_05] -- lr is [0.0001]
+[V02_05] -- Epoch 19: val_loss:0.04
+[V02_05] -- lr is [0.0001]
+[V02_05] -- Epoch 20: val_loss:0.04
+[V02_05] -- lr is [1e-05]
+[V02_05] -- Epoch 21: val_loss:0.04
+[V02_05] -- lr is [1e-05]
+[V02_05] -- Epoch 22: val_loss:0.04
+[V02_05] -- lr is [1e-05]
+[V02_05] -- Epoch 23: val_loss:0.04
+[V02_05] -- lr is [1e-05]
+[V02_05] -- Epoch 23 - Finished training at 2020_10_23_19_40_46 after 0:52:23
+[V02_05] -- best_model_fname: /ps/scratch/human_body_prior/VPoser/training_experiments/V02_05/snapshots/V02_05_epoch=13_val_loss=0.03.ckpt