add smplx misc files

10_FLAME2020/FLAME_albedo_from_BFM.npz

@@ -1,3 +0,0 @@
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-oid sha256:c6a9986024679caa881fcfe8a49687843e9338bfe0b0bb4923dccd29e01135ad
-size 1258291694

10_FLAME2020/FLAME_masks.pkl

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-version https://git-lfs.github.com/spec/v1
-oid sha256:ccefbe1ac0774ff78c68caf2c627b4abc067a6555ebeb0be5d5b0812366ab492
-size 215062

10_FLAME2020/FLAME_masks/FLAME_masks.pkl

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-oid sha256:ccefbe1ac0774ff78c68caf2c627b4abc067a6555ebeb0be5d5b0812366ab492
-size 215062

10_FLAME2020/FLAME_masks/readme

@@ -1,2 +0,0 @@
-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/flame_dynamic_embedding.npy

@@ -1,3 +0,0 @@
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-oid sha256:fd046a4740f5e6837e65622d0e050273bb71bbcb1ad6ab322474013a2a23de37
-size 42639

10_FLAME2020/generic_model.pkl

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-oid sha256:efcd14cc4a69f3a3d9af8ded80146b5b6b50df3bd74cf69108213b144eba725b
-size 53023716

10_FLAME2020/l_eyelid.npy

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-version https://git-lfs.github.com/spec/v1
-oid sha256:fa50997166c2f884fbfc577d99fe8707966763fc3d83a1870bb786df6cc9410d
-size 120680

10_FLAME2020/landmark_embedding.npy

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-oid sha256:8095348eeafce5a02f6bd8765146307f9567a3f03b316d788a2e47336d667954
-size 31292

10_FLAME2020/male_model.pkl

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-oid sha256:5a996eaca4b56b03a0c2907f7dfa3a92680d965fd190d345a7e588f6fa017d1b
-size 53023716

10_FLAME2020/r_eyelid.npy

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-version https://git-lfs.github.com/spec/v1
-oid sha256:dd78776fed765be4468889dd7d5b54b4bf61c8efbb2869b5b91409ce798aad8d
-size 120680

11_PIXIE/SMPL-X__FLAME_vertex_ids.npy

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-version https://git-lfs.github.com/spec/v1
-oid sha256:7e70cdc3659aae699b9732e8dd4af49106310c69b90dc83d9f73e96dbf871e49
-size 40312

11_PIXIE/SMPLX_NEUTRAL_2020.npz

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-version https://git-lfs.github.com/spec/v1
-oid sha256:bdf06146e27d92022fe5dadad3b9203373f6879eca8e4d8235359ee3ec6a5a74
-size 167264530

11_PIXIE/flame2smplx_tex_1024.npy

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-version https://git-lfs.github.com/spec/v1
-oid sha256:945d164becfe0f217e1a51356e843ef3f615278d66f6bbb9a7a8f70cb356c93b
-size 22043098

11_PIXIE/pixie_model.tar

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-version https://git-lfs.github.com/spec/v1
-oid sha256:9dade173cbe63527209bfd413b9698e8f194ac9bfb9cf11012a823b4c75ed6b9
-size 773605748

11_PIXIE/smplx_extra_joints.yaml

@@ -1,132 +0,0 @@
-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

@@ -1,29 +0,0 @@
-[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

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

12_vposer_v1_0/snapshots/TR00_E096.pt

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-version https://git-lfs.github.com/spec/v1
-oid sha256:0e4ad40f922606989939d3fae6eadf82d1a8e98112dffb6e39d89d6471270d5c
-size 2702962

12_vposer_v1_0/version.txt

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

12_vposer_v1_0/vposer_smpl.py

@@ -1,164 +0,0 @@
-# -*- 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: ps-license@tuebingen.mpg.de
-#
-#
-# 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

@@ -1,80 +0,0 @@
-[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

13_vposer_V02_05/V02_05.yaml

@@ -1,58 +0,0 @@
-body_model:
-  bm_path: /ps/project/common/moshpp/smplx/locked_head/model_6_merged_exp_hands_fixed_eyes/neutral/model.npz
-  gender: neutral
-data_parms:
-  amass_dir: /ps/scratch/amass/20200508/mosh_smplx_neutral
-  amass_splits:
-    test:
-    - BMLrub_test
-    train:
-    - CMU
-    - BMLrub_train
-    vald:
-    - BMLrub_vald
-  num_timeseq_frames: {}
-  num_workers: 5
-general:
-  dataset_basedir: /ps/scratch/human_body_prior/VPoser/data/smpl
-  dataset_id: V02_03
-  expr_id: V02_05
-  rnd_seed: 100
-  verbosity: 0
-  work_basedir: /ps/scratch/human_body_prior/VPoser/training_experiments
-logging:
-  best_model_fname: null
-  dataset_dir: /ps/scratch/human_body_prior/VPoser/data/smpl/V02_03
-  expr_msg: '[V02_05] batch_size = 128.'
-  num_bodies_to_display: 25
-  render_during_training: true
-  work_dir: /ps/scratch/human_body_prior/VPoser/training_experiments/V02_05
-model_params:
-  latentD: 32
-  num_neurons: 512
-train_parms:
-  batch_size: 128
-  early_stopping:
-    min_delta: 0.0
-    mode: min
-    monitor: val_loss
-    patience: 10
-    verbose: true
-  gen_optimizer:
-    args:
-      lr: 0.001
-      weight_decay: 1.0e-05
-    type: Adam
-  keep_extra_loss_terms_until_epoch: 15
-  loss_weights:
-    loss_jtr_wt: 2
-    loss_kl_wt: 0.005
-    loss_matrot_wt: 2
-    loss_rec_wt: 4
-  lr_scheduler:
-    args:
-      patience: 5
-      verbose: true
-    type: ReduceLROnPlateau
-  num_epochs: 100
-  restore_optimizer: false

13_vposer_V02_05/snapshots/V02_05_epoch=08_val_loss=0.03.ckpt

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a914aef21a5c1f4e4d1a616f28def84bc2550c4e7312a689a64e9a35eb7ebee3
-size 88644482

13_vposer_V02_05/snapshots/V02_05_epoch=13_val_loss=0.03.ckpt

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