Upload 116 files

.gitattributes

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RigNet/.gitignore

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+.idea/
+.vscode/
+__pycache__/
+utils/rigging_parser/*.pyc
+logs/
+results/
+checkpoints/
+*.html
+*.error
+*.sbatch
+*.err
+*.sh
+sideview.json
+quick_start/*.binvox
+quick_start/*_normalized*

RigNet/LICENSE-GPLv3.txt

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+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
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+  13. Use with the GNU Affero General Public License.
+
+  Notwithstanding any other provision of this License, you have
+permission to link or combine any covered work with a work licensed
+under version 3 of the GNU Affero General Public License into a single
+combined work, and to convey the resulting work.  The terms of this
+License will continue to apply to the part which is the covered work,
+but the special requirements of the GNU Affero General Public License,
+section 13, concerning interaction through a network will apply to the
+combination as such.
+
+  14. Revised Versions of this License.
+
+  The Free Software Foundation may publish revised and/or new versions of
+the GNU General Public License from time to time.  Such new versions will
+be similar in spirit to the present version, but may differ in detail to
+address new problems or concerns.
+
+  Each version is given a distinguishing version number.  If the
+Program specifies that a certain numbered version of the GNU General
+Public License "or any later version" applies to it, you have the
+option of following the terms and conditions either of that numbered
+version or of any later version published by the Free Software
+Foundation.  If the Program does not specify a version number of the
+GNU General Public License, you may choose any version ever published
+by the Free Software Foundation.
+
+  If the Program specifies that a proxy can decide which future
+versions of the GNU General Public License can be used, that proxy's
+public statement of acceptance of a version permanently authorizes you
+to choose that version for the Program.
+
+  Later license versions may give you additional or different
+permissions.  However, no additional obligations are imposed on any
+author or copyright holder as a result of your choosing to follow a
+later version.
+
+  15. Disclaimer of Warranty.
+
+  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
+APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
+HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
+OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
+THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS

RigNet/README.md

@@ -0,0 +1,112 @@
+This is the code repository implementing the paper "RigNet: Neural Rigging for Articulated Characters" published on SIGGRAPH 2020 [[Project page]](https://zhan-xu.github.io/rig-net/).
+
+**[2021.07.20]** Another add-on for Blender, 
+implemented by @[L-Medici](https://github.com/L-Medici). Please check the Github [link](https://github.com/L-Medici/Rignet_blender_addon).
+
+
+**[2020.11.23]** There is now a great add-on for Blender based on our work, 
+implemented by @[pKrime](https://github.com/pKrime). Please check the Github [link](https://github.com/pKrime/brignet), and the video [demo](https://www.youtube.com/watch?v=ueLlS3IoeGY&feature=youtu.be). 
+
+## Dependecy and Setup
+
+The project is developed on Ubuntu 16.04 with cuda10.0 and cudnn7.6.3. 
+It has also been successfully tested on Windows 10.
+On both platforms, we suggest to use conda virtual environment. 
+
+#### For Linux user
+
+**[2023.05.21]** I have tested the code on Ubuntu 22.04, with cuda 11.3. The following commands have been updated. 
+
+```
+conda create --name rignet python=3.7
+conda activate rignet
+conda install pytorch==1.12.0 torchvision==0.13.0 cudatoolkit=11.3 -c pytorch
+
+# load cuda_toolkit 11.3
+export PATH=/usr/local/cuda_11.3/bin${PATH:+:${PATH}}
+export LD_LIBRARY_PATH=/usr/local/cuda_11.3/lib64
+
+# require g++ < 10 to install the following pytorch geometric version.
+pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-1.12.0+cu113.html # this take a while
+pip install torch-geometric==1.7.2
+
+pip install numpy scipy matplotlib tensorboard open3d==0.9.0 opencv-python "rtree>=0.8,<0.9" trimesh[easy]  # Make sure to install open3d 0.9.0.
+```
+
+#### For Windows user
+
+The code has been tested on Windows 10 with cuda 10.1. The most important difference from Linux setup is, you need to download Windows-compiled Rtree from [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#rtree), and install it by
+`pip install Rtree‑0.9.4‑cp37‑cp37m‑win_amd64.whl` (64-bit system) or 
+`pip install Rtree‑0.9.4‑cp37‑cp37m‑win32.whl` (32-bit system). Other libraries can be installed in the same way as Linux setup instructions.
+
+
+
+## Quick start
+We provide a script for quick start. First download our trained models from [here](https://drive.google.com/file/d/1gM2Lerk7a2R0g9DwlK3IvCfp8c2aFVXs/view?usp=sharing). 
+Put the checkpoints folder into the project folder. 
+
+Check and run quick_start.py. We provide some examples in this script. 
+Due to randomness, the results might be slightly different among each run. 
+Generally you will get the results similar to the ones shown below:
+
+![results figure](quick_start/quick_start.png)
+
+If you want to try your own models, remember to simplify the meshes so that 
+the remeshed ones have vertices between 1K to 5K. I use quadratic edge collapse in MeshLab for this. 
+Please name the simplified meshed as *_remesh.obj.
+
+The predicted rigs are saved as *_rig.txt. You can combine the OBJ file and *_rig.txt into FBX format by 
+running maya_save_fbx.py provided by us in Maya using mayapy. (To use numpy in mayapy, download windows compiled numpy from [here](https://github.com/Eric-Vignola/numpy-for-python-2.7-64bit) and put it in mayapy library folder. For example, mine is C:\Program Files\Autodesk\Maya2019\Python\Lib\site-packages)
+
+## Data
+
+Our dataset ModelsResource-RigNetv1 has 2,703 models. 
+We split it into 80% for training (2,163‬ models), 10%
+for validation (270 models), and 10% for testing. 
+All models in fbx format can be downloaded [here](https://drive.google.com/file/d/1yojBwl5eHPqgXZ1Uh4j26S-yKK-2loPu/view?usp=sharing).
+
+To use this dataset in this project, pre-processing is performed. 
+We put the pre-processed data [here](https://drive.google.com/file/d/1-B6hJ4423rw1LrTForHp7oaG5qRAbJx3/view?usp=sharing), which consists of several sub-folders.
+
+* obj: all meshes in OBJ format.
+* rig_info: we store the rigging information into a txt file. Each txt file has four blocks. (1) Lines starting with "joint" define a joint with its 3D position. Each of joint line has four elements, which are joint_name, X, Y, and Z. (2) Line starting with "root" defines the name of root joint. (3) Lines starting with "hier" define the hierarchy of skeleton. Each hierarchy line has two elements, which are parent joint name and its child joint name. One parent joint can have multiple children joints. (4) Lines starting with "skin" define the skinning weights. Each skinning line follows the format as vertex_id, bind_joint_name_1, bind_weight_1, bind_joint_name_2, bind_weight_2 ... The vertex_id follows the vertice order in obj files in the above obj folder.
+* obj_remesh: This folder contains the obj files of the remeshed models. Meshes with fewer than 1K vertices were subdivided, and those with more than 5K vertices were simplified; as a result all training and test meshes contained between 1K and 5K vertices.
+* rig_info_remesh: Rigging information files corresponding to the remeshed obj. Joints, hierarchy and root are the same. The skinning is recalculated based on nearest neighbor from each remeshed vertex to original vertices.
+* pretrain_attention: Pre-calculated supervision to pretrin the attention module, which are calculated by the script geometric_proc/compute_pretrain_attn.py. Each file is a N-by-1 text where N is the number of vertices corresponding to remeshed OBJ file, the i-th row stores the surpervision for vertex i.  
+* volumetric_geodesic: Pre-calculated volumetric geodesic distance between each vertex-bones pair. The algorithm is an approaximation, which is implemented in geometric_proc/compute_volumetric_geodesic.py. Each file is an N-by-B numpy array where N is the number of vertices corresponding to remeshed OBJ file, B is the number of bones, and (i, j) stores the volumetric geodesic distance between vertex i and bone j. 
+* vox: voxelized models used for inside/outside check. Obtained with [binvox](https://www.patrickmin.com/binvox/). The resolution of the grid is 88x88x88.
+
+After downloading the pre-processed data, one needs to create the data directly used for training/testing, please check and run our script: 
+
+`python gen_dataset.py`
+
+Remember to change the root_folder to the directory you uncompress the pre-processed data.
+
+## Training
+
+Notes: As new features, we have three improvements from the paper: (1) To train the joint prediction module, now we pretrain both the regression module and the attention module, and then fine-tune them together with differentiable clustering. (2) We optimized the hyper-parameters in the fine-tuning step. (3) the input feature for skinning now includes another dimension per bone (--Lf), indicating whether this bone is a virtual leaf bone or not. (To enable control from the end-joints, we presume a virtual bone for them. Please check the code for more details.)
+
+1. Joint prediction: 
+
+    1.1 Pretrain regression module:
+    `python -u run_joint_pretrain.py --train_folder='DATASET_DIR/train/' --val_folder='DATASET_DIR/val/' --test_folder='DATASET_DIR/test/' --checkpoint='checkpoints/pretrain_jointnet' --logdir='logs/pretrain_jointnet' --train_batch=6 --test_batch=6 --lr 5e-4 --schedule 50 --arch='jointnet'`
+
+    1.2 Pretrain attention module:
+    `python -u run_joint_pretrain.py --train_folder='DATASET_DIR/train/' --val_folder='DATASET_DIR/val/' --test_folder='DATASET_DIR/test/' --checkpoint='checkpoints/pretrain_masknet' --logdir='logs/pretrain_masknet' --train_batch=6 --test_batch=6 --lr 1e-4 --schedule 50 --arch='masknet'`
+
+    1.3 Finetune two modules with a clustering module:
+    `python -u run_joint_finetune.py --train_folder='DATASET_DIR/train/' --val_folder='DATASET_DIR/val/' --test_folder='DATASET_DIR/test/' --checkpoint='checkpoints/gcn_meanshift' --logdir='logs/gcn_meanshift' --train_batch=1 --test_batch=1 --jointnet_lr=1e-6 --masknet_lr=1e-6 --bandwidth_lr=1e-6 --epoch=50`
+
+2. Connectivity prediction
+
+    2.1 BoneNet:
+    `python -u run_pair_cls.py --train_folder='DATASET_DIR/train/' --val_folder='DATASET_DIR/val/' --test_folder='DATASET_DIR/test/' --checkpoint='checkpoints/bonenet' --logdir='logs/bonenet' --train_batch=6 --test_batch=6 --lr=1e-3`
+
+    2.2 RootNet:
+    `python -u run_root_cls.py --train_folder='DATASET_DIR/train/' --val_folder='DATASET_DIR/val/' --test_folder='DATASET_DIR/test/' --checkpoint='checkpoints/rootnet' --logdir='logs/rootnet' --train_batch=6 --test_batch=6 --lr=1e-3`
+
+3. Skinning prediction:
+    `python -u run_skinning.py --train_folder='DATASET_DIR/train/' --val_folder='DATASET_DIR/val/' --test_folder='DATASET_DIR/test/' --checkpoint='checkpoints/skinnet' --logdir='logs/skinnet' --train_batch=4 --test_batch=4 --lr=1e-4 --Dg --Lf`
+
+##  License
+This project is under LICENSE-GPLv3.

RigNet/binvox

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RigNet/binvox.exe

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+size 1930752

RigNet/checkpoints/bonenet/model_best.pth.tar

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RigNet/checkpoints/gcn_meanshift/model_best.pth.tar

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RigNet/checkpoints/rootnet/model_best.pth.tar

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RigNet/checkpoints/skinnet/model_best.pth.tar

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RigNet/datasets/skeleton_dataset.py

@@ -0,0 +1,121 @@
+#-------------------------------------------------------------------------------
+# Name:        skeleton_dataset.py
+# Purpose:     torch_geometric dataset wrapper for skeleton training and inference
+# RigNet Copyright 2020 University of Massachusetts
+# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License.
+# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet.
+#-------------------------------------------------------------------------------
+
+import os
+import torch
+import numpy as np
+import glob
+import itertools as it
+from utils import binvox_rw
+from torch_geometric.data import Data, InMemoryDataset
+from torch_geometric.utils import add_self_loops
+
+
+class SkeletonData(Data):
+    def __init__(self, x=None, pos=None, name=None, mask=None, joints=None,
+                 tpl_edge_index=None, geo_edge_index=None, pairs=None, pair_attr=None):
+        super(SkeletonData, self).__init__()
+        self.x = x
+        self.pos = pos
+        self.name = name
+        self.mask = mask
+        self.joints = joints
+        self.tpl_edge_index = tpl_edge_index
+        self.geo_edge_index = geo_edge_index
+        self.pairs = pairs
+        self.pair_attr = pair_attr
+
+    def __inc__(self, key, value):
+        if key == 'pairs':
+            return self.joints.size(0)
+        else:
+            return super(SkeletonData, self).__inc__(key, value)
+
+
+class GraphDataset(InMemoryDataset):
+    def __init__(self, root):
+        super(GraphDataset, self).__init__(root)
+        self.data, self.slices = torch.load(self.processed_paths[0])
+
+    @property
+    def raw_file_names(self):
+        raw_v_filelist = glob.glob(os.path.join(self.root, '*_v.txt'))
+        return raw_v_filelist
+
+    @property
+    def processed_file_names(self):
+        return '{:s}_skeleton_data.pt'.format(self.root.split('/')[-1])
+
+    def __len__(self):
+        return len(self.raw_paths)
+
+    def download(self):
+        pass
+
+    def sample_on_bone(self, p_pos, ch_pos):
+        ray = ch_pos - p_pos
+        bone_length = np.sqrt(np.sum((p_pos - ch_pos) ** 2))
+        num_step = np.round(bone_length / 0.01)
+        i_step = np.arange(1, num_step + 1)
+        unit_step = ray / (num_step + 1e-30)
+        unit_step = np.repeat(unit_step[np.newaxis, :], num_step, axis=0)
+        res = p_pos + unit_step * i_step[:, np.newaxis]
+        return res
+
+    def inside_check(self, pts, vox):
+        vc = (pts - vox.translate) / vox.scale * vox.dims[0]
+        vc = np.round(vc).astype(int)
+        ind1 = np.logical_and(np.all(vc >= 0, axis=1), np.all(vc < vox.dims[0], axis=1))
+        vc = np.clip(vc, 0, vox.dims[0]-1)
+        ind2 = vox.data[vc[:, 0], vc[:, 1], vc[:, 2]]
+        ind = np.logical_and(ind1, ind2)
+        pts = pts[ind]
+        return pts, np.argwhere(ind).squeeze()
+
+    def process(self):
+        data_list = []
+        i = 0.0
+        for v_filename in self.raw_paths:
+            print('preprecessing data complete: {:.4f}%'.format(100 * i / len(self.raw_paths)))
+            i += 1.0
+            v = np.loadtxt(v_filename)
+            m = np.loadtxt(v_filename.replace('_v.txt', '_attn.txt'))
+            tpl_e = np.loadtxt(v_filename.replace('_v.txt', '_tpl_e.txt')).T
+            geo_e = np.loadtxt(v_filename.replace('_v.txt', '_geo_e.txt')).T
+            joints = np.loadtxt(v_filename.replace('_v.txt', '_j.txt'))
+            adj = np.loadtxt(v_filename.replace('_v.txt', '_adj.txt'), dtype=np.uint8)
+
+            vox_file = v_filename.replace('_v.txt', '.binvox')
+            with open(vox_file, 'rb') as fvox:
+                vox = binvox_rw.read_as_3d_array(fvox)
+            pairs = list(it.combinations(range(adj.shape[0]), 2))
+            pair_attr = []
+            for pr in pairs:
+                dist = np.linalg.norm(joints[pr[0]] - joints[pr[1]])
+                bone_samples = self.sample_on_bone(joints[pr[0]], joints[pr[1]])
+                bone_samples_inside, _ = self.inside_check(bone_samples, vox)
+                outside_proportion = len(bone_samples_inside) / (len(bone_samples) + 1e-10)
+                attr = np.array([dist, outside_proportion, adj[pr[0], pr[1]]])
+                pair_attr.append(attr)
+            pairs = np.array(pairs)
+            pair_attr = np.array(pair_attr)
+            name = int(v_filename.split('/')[-1].split('_')[0])
+
+            v = torch.from_numpy(v).float()
+            m = torch.from_numpy(m).long()
+            tpl_e = torch.from_numpy(tpl_e).long()
+            geo_e = torch.from_numpy(geo_e).long()
+            tpl_e, _ = add_self_loops(tpl_e, num_nodes=v.size(0))
+            geo_e, _ = add_self_loops(geo_e, num_nodes=v.size(0))
+            joints = torch.from_numpy(joints).float()
+            pairs = torch.from_numpy(pairs).float()
+            pair_attr = torch.from_numpy(pair_attr).float()
+            data_list.append(SkeletonData(x=v[:, 3:6], pos=v[:, 0:3], name=name, mask=m, joints=joints,
+                                          tpl_edge_index=tpl_e, geo_edge_index=geo_e, pairs=pairs, pair_attr=pair_attr))
+        data, slices = self.collate(data_list)
+        torch.save((data, slices), self.processed_paths[0])

RigNet/datasets/skin_dataset.py

@@ -0,0 +1,110 @@
+#-------------------------------------------------------------------------------
+# Name:        skin_dataset.py
+# Purpose:     torch_geometric dataset wrapper for skinning training and inference
+# RigNet Copyright 2020 University of Massachusetts
+# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License.
+# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet.
+#-------------------------------------------------------------------------------
+
+import os
+import torch
+import numpy as np
+import glob
+from torch_geometric.data import Data, InMemoryDataset
+from torch_geometric.utils import add_self_loops
+
+
+class SkinDataset(InMemoryDataset):
+    def __init__(self, root):
+        super(SkinDataset, self).__init__(root)
+        self.data, self.slices = torch.load(self.processed_paths[0])
+
+    @property
+    def raw_file_names(self):
+        raw_v_filelist = glob.glob(os.path.join(self.root, '*_v.txt'))
+        return raw_v_filelist
+
+    @property
+    def processed_file_names(self):
+        return '{:s}_skinning_data.pt'.format(self.root.split('/')[-1])
+
+    def __len__(self):
+        return len(self.raw_paths)
+
+    def download(self):
+        pass
+
+    def load_skin(self, filename):
+        with open(filename, 'r') as fin:
+            lines = fin.readlines()
+        bones = []
+        input = []
+        label = []
+        nearest_bone_ids = []
+        loss_mask_all = []
+        for li in lines:
+            words = li.strip().split()
+            if words[0] == 'bones':
+                bones.append([float(w) for w in words[3:]])
+            elif words[0] == 'bind':
+                words = [float(w) for w in words[1:]]
+                sample_input = []
+                sample_nearest_bone_ids = []
+                loss_mask = []
+                for i in range(self.num_nearest_bone):
+                    if int(words[3 * i + 1]) == -1:
+                        ## walk-round. however words[3] may also be invalid.
+                        sample_nearest_bone_ids.append(int(words[1]))
+                        sample_input += bones[int(words[1])]
+                        sample_input.append(words[2])
+                        sample_input.append(int(words[3]))
+                        loss_mask.append(0)
+                    else:
+                        sample_nearest_bone_ids.append(int(words[3 * i + 1]))
+                        sample_input += bones[int(words[3 * i + 1])]
+                        sample_input.append(words[3 * i + 2])
+                        sample_input.append(int(words[3 * i + 3]))
+                        loss_mask.append(1)
+                input.append(np.array(sample_input)[np.newaxis, :])
+                nearest_bone_ids.append(np.array(sample_nearest_bone_ids)[np.newaxis, :])
+                loss_mask_all.append(np.array(loss_mask)[np.newaxis, :])
+            elif words[0] == 'influence':
+                sample_label = np.array([float(w) for w in words[1:]])[np.newaxis, :]
+                label.append(sample_label)
+
+        input = np.concatenate(input, axis=0)
+        nearest_bone_ids = np.concatenate(nearest_bone_ids, axis=0)
+        label = np.concatenate(label, axis=0)
+        loss_mask_all = np.concatenate(loss_mask_all, axis=0)
+
+        return input, nearest_bone_ids, label, loss_mask_all
+
+    def process(self):
+        data_list = []
+        self.num_nearest_bone = 5
+        i = 0.0
+        for v_filename in self.raw_paths:
+            print('preprecessing data complete: {:.4f}%'.format(100 * i / len(self.raw_paths)))
+            i += 1.0
+            v = np.loadtxt(v_filename)
+            v = torch.from_numpy(v).float()
+            tpl_e = np.loadtxt(v_filename.replace('_v.txt', '_tpl_e.txt')).T
+            geo_e = np.loadtxt(v_filename.replace('_v.txt', '_geo_e.txt')).T
+            tpl_e = torch.from_numpy(tpl_e).long()
+            geo_e = torch.from_numpy(geo_e).long()
+            tpl_e, _ = add_self_loops(tpl_e, num_nodes=v.size(0))
+            geo_e, _ = add_self_loops(geo_e, num_nodes=v.size(0))
+            skin_input, skin_nn, skin_label, loss_mask = self.load_skin(v_filename.replace('_v.txt', '_skin.txt'))
+            
+            skin_input = torch.from_numpy(skin_input).float()
+            skin_label = torch.from_numpy(skin_label).float()
+            skin_nn = torch.from_numpy(skin_nn).long()
+            loss_mask = torch.from_numpy(loss_mask).long()
+            num_skin = len(skin_input)
+
+            name = int(v_filename.split('/')[-1].split('_')[0])
+            data_list.append(Data(x=v[:, 3:6], pos=v[:, 0:3], skin_input=skin_input, skin_label=skin_label,
+                                  skin_nn=skin_nn, loss_mask=loss_mask, num_skin=num_skin, name=name,
+                                  tpl_edge_index=tpl_e, geo_edge_index=geo_e))
+        data, slices = self.collate(data_list)
+        torch.save((data, slices), self.processed_paths[0])

RigNet/gen_dataset.py

@@ -0,0 +1,169 @@
+#-------------------------------------------------------------------------------
+# Name:        gen_dataset.py
+# Purpose:     Script to generate data for skeleton and connectivity predition stage
+#              Change dataset_folder to the folder where you put the downloaded pre-processed data
+# RigNet Copyright 2020 University of Massachusetts
+# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License.
+# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet.
+#-------------------------------------------------------------------------------
+
+import os
+import shutil
+import numpy as np
+import open3d as o3d
+from multiprocessing import Pool
+from utils.io_utils import mkdir_p
+from utils.rig_parser import Info
+from geometric_proc.common_ops import calc_surface_geodesic, get_bones
+
+
+def get_tpl_edges(remesh_obj_v, remesh_obj_f):
+    edge_index = []
+    for v in range(len(remesh_obj_v)):
+        face_ids = np.argwhere(remesh_obj_f == v)[:, 0]
+        neighbor_ids = []
+        for face_id in face_ids:
+            for v_id in range(3):
+                if remesh_obj_f[face_id, v_id] != v:
+                    neighbor_ids.append(remesh_obj_f[face_id, v_id])
+        neighbor_ids = list(set(neighbor_ids))
+        neighbor_ids = [np.array([v, n])[np.newaxis, :] for n in neighbor_ids]
+        if len(neighbor_ids) == 0:
+            continue
+        neighbor_ids = np.concatenate(neighbor_ids, axis=0)
+        edge_index.append(neighbor_ids)
+    edge_index = np.concatenate(edge_index, axis=0)
+    return edge_index
+
+
+def get_geo_edges(surface_geodesic, remesh_obj_v):
+    edge_index = []
+    surface_geodesic += 1.0 * np.eye(len(surface_geodesic))  # remove self-loop edge here
+    for i in range(len(remesh_obj_v)):
+        geodesic_ball_samples = np.argwhere(surface_geodesic[i, :] <= 0.06).squeeze(1)
+        if len(geodesic_ball_samples) > 10:
+            geodesic_ball_samples = np.random.choice(geodesic_ball_samples, 10, replace=False)
+        edge_index.append(np.concatenate((np.repeat(i, len(geodesic_ball_samples))[:, np.newaxis],
+                                          geodesic_ball_samples[:, np.newaxis]), axis=1))
+    edge_index = np.concatenate(edge_index, axis=0)
+    return edge_index
+
+
+def genDataset(process_id):
+    global dataset_folder
+    print("process ID {:d}".format(process_id))
+    if process_id < 6:
+        model_list = np.loadtxt(os.path.join(dataset_folder, 'train_final.txt'), dtype=int)
+        model_list = model_list[365*process_id: 365*(process_id+1)]
+        split_name = 'train'
+    elif process_id == 6:
+        model_list = np.loadtxt(os.path.join(dataset_folder, 'val_final.txt'), dtype=int)
+        split_name = 'val'
+    elif process_id == 7:
+        model_list = np.loadtxt(os.path.join(dataset_folder, 'test_final.txt'), dtype=int)
+        split_name = 'test'
+
+    mkdir_p(os.path.join(dataset_folder, split_name))
+    for model_id in model_list:
+        remeshed_obj_filename = os.path.join(dataset_folder, 'obj_remesh/{:d}.obj'.format(model_id))
+        info_filename = os.path.join(dataset_folder, 'rig_info_remesh/{:d}.txt'.format(model_id))
+        remeshed_obj = o3d.io.read_triangle_mesh(remeshed_obj_filename)
+        remesh_obj_v = np.asarray(remeshed_obj.vertices)
+        if not remeshed_obj.has_vertex_normals():
+            remeshed_obj.compute_vertex_normals()
+        remesh_obj_vn = np.asarray(remeshed_obj.vertex_normals)
+        remesh_obj_f = np.asarray(remeshed_obj.triangles)
+        rig_info = Info(info_filename)
+
+        #vertices
+        vert_filename = os.path.join(dataset_folder, '{:s}/{:d}_v.txt'.format(split_name, model_id))
+        input_feature = np.concatenate((remesh_obj_v, remesh_obj_vn), axis=1)
+        np.savetxt(vert_filename, input_feature, fmt='%.6f')
+
+        #topology edges
+        edge_index = get_tpl_edges(remesh_obj_v, remesh_obj_f)
+        graph_filename = os.path.join(dataset_folder, '{:s}/{:d}_tpl_e.txt'.format(split_name, model_id))
+        np.savetxt(graph_filename, edge_index, fmt='%d')
+
+        # geodesic_edges
+        surface_geodesic = calc_surface_geodesic(remeshed_obj)
+        edge_index = get_geo_edges(surface_geodesic, remesh_obj_v)
+        graph_filename = os.path.join(dataset_folder, '{:s}/{:d}_geo_e.txt'.format(split_name, model_id))
+        np.savetxt(graph_filename, edge_index, fmt='%d')
+
+        # joints
+        joint_pos = rig_info.get_joint_dict()
+        joint_name_list = list(joint_pos.keys())
+        joint_pos_list = list(joint_pos.values())
+        joint_pos_list = [np.array(i) for i in joint_pos_list]
+        adjacent_matrix = rig_info.adjacent_matrix()
+        joint_filename = os.path.join(dataset_folder, '{:s}/{:d}_j.txt'.format(split_name, model_id))
+        adj_filename = os.path.join(dataset_folder, '{:s}/{:d}_adj.txt'.format(split_name, model_id))
+        np.savetxt(adj_filename, adjacent_matrix, fmt='%d')
+        np.savetxt(joint_filename, np.array(joint_pos_list), fmt='%.6f')
+
+        # pre_trained attn
+        shutil.copyfile(os.path.join(dataset_folder, 'pretrain_attention/{:d}.txt'.format(model_id)), 
+                        os.path.join(dataset_folder, '{:s}/{:d}_attn.txt'.format(split_name, model_id)))
+        
+        # voxel
+        shutil.copyfile(os.path.join(dataset_folder, 'vox/{:d}.binvox'.format(model_id)), 
+                        os.path.join(dataset_folder, '{:s}/{:d}.binvox'.format(split_name, model_id)))
+
+        #skinning information
+        num_nearest_bone = 5
+        geo_dist = np.load(os.path.join(dataset_folder, "volumetric_geodesic/{:d}_volumetric_geo.npy".format(model_id)))
+        bone_pos, bone_names, bone_isleaf = get_bones(rig_info)
+
+        input_samples = []  # mesh_vertex_id, (bone_id, 1 / D_g, is_leaf) * N
+        ground_truth_labels = []  # w_1, w_2, ..., w_N
+        for vert_remesh_id in range(len(remesh_obj_v)):
+            this_sample = [vert_remesh_id]
+            this_label = []
+            skin = rig_info.joint_skin[vert_remesh_id]
+            skin_w = {}
+            for i in np.arange(1, len(skin), 2):
+                skin_w[skin[i]] = float(skin[i + 1])
+            bone_id_near_to_far = np.argsort(geo_dist[vert_remesh_id, :])
+            for i in range(num_nearest_bone):
+                if i >= len(bone_id_near_to_far):
+                    this_sample += [-1, 0, 0]
+                    this_label.append(0.0)
+                    continue
+                bone_id = bone_id_near_to_far[i]
+                this_sample.append(bone_id)
+                this_sample.append(1.0 / (geo_dist[vert_remesh_id, bone_id] + 1e-10))
+                this_sample.append(bone_isleaf[bone_id])
+                start_joint_name = bone_names[bone_id][0]
+                if start_joint_name in skin_w:
+                    this_label.append(skin_w[start_joint_name])
+                    del skin_w[start_joint_name]
+                else:
+                    this_label.append(0.0)
+
+            input_samples.append(this_sample)
+            ground_truth_labels.append(this_label)
+
+        with open(os.path.join(dataset_folder, '{:s}/{:d}_skin.txt'.format(split_name, model_id)), 'w') as fout:
+            for i in range(len(bone_pos)):
+                fout.write('bones {:s} {:s} {:.6f} {:.6f} {:.6f} '
+                           '{:.6f} {:.6f} {:.6f}\n'.format(bone_names[i][0], bone_names[i][1],
+                                                           bone_pos[i, 0], bone_pos[i, 1], bone_pos[i, 2],
+                                                           bone_pos[i, 3], bone_pos[i, 4], bone_pos[i, 5]))
+            for i in range(len(input_samples)):
+                fout.write('bind {:d} '.format(input_samples[i][0]))
+                for j in np.arange(1, len(input_samples[i]), 3):
+                    fout.write('{:d} {:.6f} {:d} '.format(input_samples[i][j], input_samples[i][j + 1], input_samples[i][j + 2]))
+                fout.write('\n')
+            for i in range(len(ground_truth_labels)):
+                fout.write('influence ')
+                for j in range(len(ground_truth_labels[i])):
+                    fout.write('{:.3f} '.format(ground_truth_labels[i][j]))
+                fout.write('\n')
+
+
+if __name__ == '__main__':
+    dataset_folder = "/media/zhanxu/4T/ModelResource_RigNetv1_preproccessed/"
+    p = Pool(8)
+    p.map(genDataset, [0, 1, 2, 3, 4, 5, 6, 7])
+    #genDataset(0)

RigNet/geometric_proc/common_ops.py

@@ -0,0 +1,83 @@
+#-------------------------------------------------------------------------------
+# Name:        common_ops.py
+# Purpose:     common functions for geometry processing
+# RigNet Copyright 2020 University of Massachusetts
+# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License.
+# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet.
+#-------------------------------------------------------------------------------
+
+import numpy as np
+import time
+import open3d as o3d
+from scipy.sparse import lil_matrix
+from scipy.sparse.csgraph import dijkstra
+
+
+def get_bones(skel):
+    """
+    extract bones from skeleton struction
+    :param skel: input skeleton
+    :return: bones are B*6 array where each row consists starting and ending points of a bone
+             bone_name are a list of B elements, where each element consists starting and ending joint name
+             leaf_bones indicate if this bone is a virtual "leaf" bone.
+             We add virtual "leaf" bones to the leaf joints since they always have skinning weights as well
+    """
+    bones = []
+    bone_name = []
+    leaf_bones = []
+    this_level = [skel.root]
+    while this_level:
+        next_level = []
+        for p_node in this_level:
+            p_pos = np.array(p_node.pos)
+            next_level += p_node.children
+            for c_node in p_node.children:
+                c_pos = np.array(c_node.pos)
+                bones.append(np.concatenate((p_pos, c_pos))[np.newaxis, :])
+                bone_name.append([p_node.name, c_node.name])
+                leaf_bones.append(False)
+                if len(c_node.children) == 0:
+                    bones.append(np.concatenate((c_pos, c_pos))[np.newaxis, :])
+                    bone_name.append([c_node.name, c_node.name+'_leaf'])
+                    leaf_bones.append(True)
+        this_level = next_level
+    bones = np.concatenate(bones, axis=0)
+    return bones, bone_name, leaf_bones
+
+
+def calc_surface_geodesic(mesh):
+    # We denselu sample 4000 points to be more accuracy.
+    samples = mesh.sample_points_poisson_disk(number_of_points=4000)
+    pts = np.asarray(samples.points)
+    pts_normal = np.asarray(samples.normals)
+
+    time1 = time.time()
+    N = len(pts)
+    verts_dist = np.sqrt(np.sum((pts[np.newaxis, ...] - pts[:, np.newaxis, :]) ** 2, axis=2))
+    verts_nn = np.argsort(verts_dist, axis=1)
+    conn_matrix = lil_matrix((N, N), dtype=np.float32)
+
+    for p in range(N):
+        nn_p = verts_nn[p, 1:6]
+        norm_nn_p = np.linalg.norm(pts_normal[nn_p], axis=1)
+        norm_p = np.linalg.norm(pts_normal[p])
+        cos_similar = np.dot(pts_normal[nn_p], pts_normal[p]) / (norm_nn_p * norm_p + 1e-10)
+        nn_p = nn_p[cos_similar > -0.5]
+        conn_matrix[p, nn_p] = verts_dist[p, nn_p]
+    [dist, predecessors] = dijkstra(conn_matrix, directed=False, indices=range(N),
+                                    return_predecessors=True, unweighted=False)
+
+    # replace inf distance with euclidean distance + 8
+    # 6.12 is the maximal geodesic distance without considering inf, I add 8 to be safer.
+    inf_pos = np.argwhere(np.isinf(dist))
+    if len(inf_pos) > 0:
+        euc_distance = np.sqrt(np.sum((pts[np.newaxis, ...] - pts[:, np.newaxis, :]) ** 2, axis=2))
+        dist[inf_pos[:, 0], inf_pos[:, 1]] = 8.0 + euc_distance[inf_pos[:, 0], inf_pos[:, 1]]
+
+    verts = np.array(mesh.vertices)
+    vert_pts_distance = np.sqrt(np.sum((verts[np.newaxis, ...] - pts[:, np.newaxis, :]) ** 2, axis=2))
+    vert_pts_nn = np.argmin(vert_pts_distance, axis=0)
+    surface_geodesic = dist[vert_pts_nn, :][:, vert_pts_nn]
+    time2 = time.time()
+    print('surface geodesic calculation: {} seconds'.format((time2 - time1)))
+    return surface_geodesic

RigNet/geometric_proc/compute_pretrain_attn.py

@@ -0,0 +1,279 @@
+#-------------------------------------------------------------------------------
+# Name:        compute_pretrain_attn.py
+# Purpose:     This script prepares the supervision for attention module pretraining.
+#              It shoots rays from each joint around the plane perpendicular to the bone, which hit the surface.
+#              Vertices near the valid hits are marked as 1, otherwise 0.
+# RigNet Copyright 2020 University of Massachusetts
+# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License.
+# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet.
+#-------------------------------------------------------------------------------
+
+"""
+
+"""
+
+import sys
+sys.path.append("./")
+import os
+import glob
+import time
+import copy
+import trimesh
+import numpy as np
+import open3d as o3d
+from utils.rig_parser import Info
+
+
+def get_perpend_vec(v):
+    max_dim = np.argmax(np.abs(v))
+    if max_dim == 0:
+        u_0 = np.array([(-2.0 * v[1] - 1.0 * v[2]) / (v[0]+1e-10), 2.0, 1.0])
+    elif max_dim == 1:
+        u_0 = np.array([1.0, (-1.0 * v[0] - 2.0 * v[2]) / (v[1]+1e-10), 2.0])
+    elif max_dim == 2:
+        u_0 = np.array([1.0, 2.0, (-1.0 * v[0] - 2.0 * v[1]) / (v[2]+1e-10)])
+    u_0 /= np.linalg.norm(u_0)
+    return u_0
+
+
+def cal_perpendicular_dir(p_pos, ch_pos):
+    global ray_per_sample
+    dirs = []
+    v = (ch_pos - p_pos).squeeze()
+    v = v / (np.linalg.norm(v)+1e-10)
+    u_0 = get_perpend_vec(v)
+    w = np.cross(v, u_0)
+    w = w / (np.linalg.norm(w)+1e-10)
+    for angle in np.arange(0, 2*np.pi, 2*np.pi/ray_per_sample):
+        u = np.cos(angle) * u_0 + np.sin(angle) * w
+        u = u / (np.linalg.norm(u)+1e-10)
+        dirs.append(u[np.newaxis, :])
+    dirs = np.concatenate(dirs, axis=0)
+    return dirs
+
+
+def form_rays(skel):
+    '''
+    generate rays from joints, with perpendicular direction
+    :param skel: input skeleton
+    :return: ray origins and ray directions
+    '''
+    origins = []
+    dirs = []
+    this_level = [skel.root]
+    while this_level:
+        next_level = []
+        for p_node in this_level:
+            next_level += p_node.children
+            p_pos = np.array(p_node.pos)[np.newaxis, :]
+            for c_node in p_node.children:
+                c_pos = np.array(c_node.pos)[np.newaxis, :]
+                origin_bone = np.concatenate(([p_pos, c_pos]), axis=0)
+                dir_bone = cal_perpendicular_dir(p_pos, c_pos)
+                origins.append(np.repeat(origin_bone, len(dir_bone), axis=0))
+                dirs.append(np.tile(dir_bone, (len(origin_bone), 1)))
+        this_level = next_level
+    origins = np.concatenate(origins, axis=0)
+    dirs = np.concatenate(dirs, axis=0)
+    return origins, dirs
+
+
+def shoot_rays(mesh, origins, ray_dir, debug=False, model_id=None):
+    '''
+    shoot rays and record the first hit distance, as well as all vertices on the hit faces.
+    :param mesh: input mesh (trimesh)
+    :param origins: origin of rays
+    :param ray_dir: direction of rays
+    :return: all vertices indices on the hit face, the distance of first hit for each ray.
+    '''
+    global ray_per_sample
+    RayMeshIntersector = trimesh.ray.ray_triangle.RayMeshIntersector(mesh)
+    locations, index_ray, index_tri = RayMeshIntersector.intersects_location(origins, ray_dir + 1e-15)
+    locations_per_ray = []
+    index_tri_per_ray = []
+    for i in range(len(ray_dir)):
+        locations_per_ray.append(locations[index_ray == i])
+        index_tri_per_ray.append(index_tri[index_ray == i])
+    all_hit_pos = []
+    all_hit_tri = []
+    all_hit_ori = []
+    all_hit_ori_id = []
+    for ori_id in np.arange(0, len(ray_dir), ray_per_sample):
+        hit_pos = []
+        hit_tri = []
+        hit_dist = []
+        hit_ori_id = []
+        for i in range(ray_per_sample):
+            ray_id = int(ori_id + i)
+            if len(locations_per_ray[ray_id]) > 1:
+                closest_hit_id = np.argmin(np.linalg.norm(locations_per_ray[ray_id] - origins[ray_id], axis=1))
+                hit_pos.append(locations_per_ray[ray_id][closest_hit_id][np.newaxis, :])
+                hit_dist.append(np.linalg.norm(locations_per_ray[ray_id][closest_hit_id] - origins[ray_id]))
+                hit_tri.append(index_tri_per_ray[ray_id][closest_hit_id])
+                hit_ori_id.append(int(ori_id/ray_per_sample))
+            elif len(locations_per_ray[ray_id]) == 1:
+                hit_pos.append(locations_per_ray[ray_id])
+                hit_dist.append(np.linalg.norm(locations_per_ray[ray_id][0] - origins[ray_id]))
+                hit_tri.append(index_tri_per_ray[ray_id][0])
+                hit_ori_id.append(int(ori_id/ray_per_sample))
+
+        if len(hit_pos) == 0: # no hit, pick nearby faces
+            hit_tri = trimesh.proximity.nearby_faces(mesh, origins[int(ori_id + 0)][np.newaxis, :])[0]
+            hit_vertices = mesh.faces[hit_tri].flatten()
+            hit_pos = [np.array(mesh.vertices[i])[np.newaxis, :] for i in hit_vertices]
+            hit_dist = [np.linalg.norm(hit_pos[i].squeeze() - origins[int(ori_id + 0)]) for i in range(len(hit_pos))]
+            hit_tri = np.repeat(hit_tri, 3)
+            hit_ori_id = np.repeat(int(ori_id / ray_per_sample), len(hit_tri))
+
+        hit_pos = np.concatenate(hit_pos, axis=0)
+        hit_dist = np.array(hit_dist)
+        hit_tri = np.array(hit_tri)
+        hit_ori_id = np.array(hit_ori_id)
+        valid_ids = np.argwhere(hit_dist < np.percentile(hit_dist, 20) * 2).squeeze(1)
+        hit_pos = hit_pos[valid_ids]
+        hit_dist = hit_dist[valid_ids]
+        hit_tri = hit_tri[valid_ids]
+        hit_ori_id = hit_ori_id[valid_ids]
+
+        all_hit_pos.append(hit_pos)
+        all_hit_tri.append(hit_tri)
+        all_hit_ori_id.append(hit_ori_id)
+        all_hit_ori.append(origins[int(ori_id + 0)][np.newaxis, :])
+
+    all_hit_pos = np.concatenate(all_hit_pos, axis=0)
+    all_hit_tri = np.concatenate(all_hit_tri)
+    all_hit_ori_id = np.concatenate(all_hit_ori_id)
+    all_hit_ori = np.concatenate(all_hit_ori, axis=0)
+
+    if debug:
+        import open3d as o3d
+        from utils.vis_utils import find_lines_from_tree, drawSphere
+        mesh_filename = 'dataset_folder/obj/{:d}.obj'.format(model_id)
+        skel = Info('dataset_folder/rig_info/{:d}.txt'.format(model_id))
+        # show mesh
+        mesh_o3d = o3d.io.read_triangle_mesh(mesh_filename)
+        mesh_ls = o3d.geometry.LineSet.create_from_triangle_mesh(mesh_o3d)
+        mesh_ls.colors = o3d.utility.Vector3dVector([[0.8, 0.8, 0.8] for i in range(len(mesh_ls.lines))])
+        # show skeleton
+        line_list_skel = []
+        joint_pos_list = []
+        find_lines_from_tree(skel.root, line_list_skel, joint_pos_list)
+        line_set_skel = o3d.geometry.LineSet()
+        line_set_skel.points = o3d.utility.Vector3dVector(joint_pos_list)
+        line_set_skel.lines = o3d.utility.Vector2iVector(line_list_skel)
+        colors = [[1.0, 0.0, 0.0] for i in range(len(line_list_skel))]
+        line_set_skel.colors = o3d.utility.Vector3dVector(colors)
+        # show ray
+        dpts = np.concatenate((all_hit_ori, all_hit_pos), axis=0)
+        dlines = o3d.geometry.LineSet()
+        dlines.points = o3d.utility.Vector3dVector(dpts)
+        dlines.lines = o3d.utility.Vector2iVector([[all_hit_ori_id[i], len(all_hit_ori) + i] for i in range(len(all_hit_ori_id))])
+        colors = [[0.0, 0.0, 1.0] for i in range(len(all_hit_ori_id))]
+        dlines.colors = o3d.utility.Vector3dVector(colors)
+        vis = o3d.visualization.Visualizer()
+        vis.create_window()
+        vis.add_geometry(dlines)
+        vis.add_geometry(mesh_ls)
+        vis.add_geometry(line_set_skel)
+        this_level = skel.root.children
+        while this_level:
+            next_level = []
+            for p_node in this_level:
+                vis.add_geometry(drawSphere(p_node.pos, 0.007, color=[1.0, 0.0, 0.0]))  # [0.3, 0.1, 0.1]
+                next_level += p_node.children
+            this_level = next_level
+        vis.run()
+        vis.destroy_window()
+
+    return all_hit_pos, all_hit_ori_id, all_hit_ori
+
+
+def normalize_mesh_rig(mesh, rig):
+    # normalize mesh
+    mesh_v = np.asarray(mesh.vertices)
+    dims = [max(mesh_v[:, 0]) - min(mesh_v[:, 0]),
+            max(mesh_v[:, 1]) - min(mesh_v[:, 1]),
+            max(mesh_v[:, 2]) - min(mesh_v[:, 2])]
+    scale = 1.0 / max(dims)
+    pivot = np.array([(min(mesh_v[:, 0]) + max(mesh_v[:, 0])) / 2, min(mesh_v[:, 1]),
+                      (min(mesh_v[:, 2]) + max(mesh_v[:, 2])) / 2])
+    mesh_v[:, 0] -= pivot[0]
+    mesh_v[:, 1] -= pivot[1]
+    mesh_v[:, 2] -= pivot[2]
+    mesh_v *= scale
+    mesh.vertices = o3d.utility.Vector3dVector(mesh_v)
+
+    # normalize rig
+    for k, v in rig.joint_pos.items():
+        rig.joint_pos[k] -= pivot
+        rig.joint_pos[k] *= scale
+    this_level = [rig.root]
+    while this_level:
+        next_level = []
+        for node in this_level:
+            node.pos = (np.array(node.pos) - pivot) * scale
+            node.pos = (node.pos[0], node.pos[1], node.pos[2])
+            for ch in node.children:
+                next_level.append(ch)
+        this_level = next_level
+
+    return mesh, rig
+
+if __name__ == '__main__':
+    start_id = int(sys.argv[1])
+    end_id = int(sys.argv[2])
+    subsampling = True # decimate mesh to speed up
+
+    ray_per_sample = 14 # number of rays shoot from each joint
+    dataset_folder = "/media/zhanxu/4T/ModelResource_RigNetv1_preproccessed/"
+    #dataset_folder = "/home/zhanxu/Proj/RigNet_public/quick_start/tigran/"
+
+    remesh_obj_folder = os.path.join(dataset_folder, "obj_remesh/")
+    info_folder = os.path.join(dataset_folder, "rig_info/")
+    res_folder = os.path.join(dataset_folder, "pretrain_attention/")
+    model_list = np.loadtxt(os.path.join(dataset_folder, "model_list.txt"), dtype=int)
+    #model_list = np.array([1, 2, 3, 4, 5, 6, 7], dtype=np.int)
+
+    for model_id in model_list[start_id:end_id]:
+        print(model_id)
+        mesh = o3d.io.read_triangle_mesh(os.path.join(remesh_obj_folder, '{:d}.obj'.format(model_id)))
+        rig_info = Info(os.path.join(info_folder, '{:d}.txt'.format(model_id)))
+        mesh, rig_info = normalize_mesh_rig(mesh, rig_info)
+        mesh_ori = copy.deepcopy(mesh)
+        vtx_ori = np.asarray(mesh.vertices)
+
+        if subsampling:
+            mesh = mesh.simplify_quadric_decimation(3000)
+
+        mesh_trimesh = trimesh.Trimesh(vertices=np.asarray(mesh.vertices), faces=np.asarray(mesh.triangles), process=False)
+        trimesh.repair.fix_normals(mesh_trimesh)
+
+        origins, dirs = form_rays(rig_info)
+        hit_pos, all_hit_ori_id, all_hit_ori = shoot_rays(mesh_trimesh, origins, dirs, debug=False, model_id=model_id)
+
+        dist = np.sqrt(np.sum((vtx_ori[np.newaxis, ...] - hit_pos[:, np.newaxis, :])**2, axis=2))
+        dist = (dist < 2e-2)
+
+        attn = np.zeros(len(vtx_ori), np.bool)
+        for joint_id in np.unique(all_hit_ori_id):
+            num_nn = np.sum(np.sum(dist[np.argwhere(all_hit_ori_id == joint_id).squeeze(), :], axis=0) > 0)
+            if num_nn < 6:
+                # too few nearest points
+                id_sort = np.argsort(np.linalg.norm(vtx_ori - all_hit_ori[joint_id][np.newaxis, :], axis=1))
+                attn[id_sort[0:6]] = True
+            else:
+                id_nn = np.argwhere(np.sum(dist[np.argwhere(all_hit_ori_id == joint_id).squeeze(), :], axis=0) > 0).squeeze(1)
+                attn[id_nn] = True
+
+        # vis = o3d.visualization.Visualizer()
+        # vis.create_window()
+        # mesh_ls = o3d.geometry.LineSet.create_from_triangle_mesh(mesh_ori)
+        # mesh_ls.colors = o3d.utility.Vector3dVector([[0.8, 0.8, 0.8] for i in range(len(mesh_ls.lines))])
+        # vis.add_geometry(mesh_ls)
+        # pcd = o3d.geometry.PointCloud(points=o3d.utility.Vector3dVector(vtx_ori[np.argwhere(attn).squeeze()]))
+        # pcd.paint_uniform_color([1.0, 0.0, 0.0])
+        # vis.add_geometry(pcd)
+        # vis.run()
+        # vis.destroy_window()
+
+        np.savetxt(os.path.join(res_folder, '{:d}.txt'.format(model_id)), attn, fmt='%d')

RigNet/geometric_proc/compute_surface_geodesic.py

@@ -0,0 +1,40 @@
+#-------------------------------------------------------------------------------
+# Name:        compute_surface_geodesic.py
+# Purpose:     This script calculates surface geodesic distance between all pair of vertices.
+#              It doesn't rely on mesh topology. Surface is densely sampled to form a graph and get shortest path between samples.
+#              Geodesic distance between pair of vertices are the geodesic distance between nearest samples to both vertices.
+# RigNet Copyright 2020 University of Massachusetts
+# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License.
+# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet.
+#-------------------------------------------------------------------------------
+
+import sys
+sys.path.append("./")
+import os
+import glob
+import numpy as np
+import open3d as o3d
+from multiprocessing import Pool
+from geometric_proc.common_ops import calc_surface_geodesic
+
+
+def one_process(process_id):
+    start_id = process_id * 340
+    end_id = (process_id + 1) * 340
+    print("processing {:d} to {:d}\n".format(start_id, end_id))
+
+    remesh_obj_folder = "/media/zhanxu/4T1/ModelResource_Dataset/obj_remesh/"
+    res_folder = "/media/zhanxu/4T1/ModelResource_Dataset/surface_geodesic/"
+
+    remesh_obj_folder = glob.glob(remesh_obj_folder + '*.obj')
+    for remesh_obj_filename in remesh_obj_folder[start_id: end_id]:
+        model_id = remesh_obj_filename.split('/')[-1].split('.')[0]
+        print(model_id)
+        surface_geodesic = calc_surface_geodesic(o3d.io.read_triangle_mesh(remesh_obj_filename))
+        np.save(os.path.join(res_folder, "{:s}_surface_geo.npy".format(model_id)), surface_geodesic.astype(np.float16))
+
+
+if __name__ == '__main__':
+    #one_process(0)
+    p = Pool(4)
+    p.map(one_process, [0, 1, 2, 3])

RigNet/geometric_proc/compute_volumetric_geodesic.py

@@ -0,0 +1,173 @@
+#-------------------------------------------------------------------------------
+# Name:        compute_volumetric_geodesic.py
+# Purpose:     his script calculates volumetric geodesic distance between vertices and bones.
+#              The shortest paths start from bones, then hit all "visible" vertices w.r.t each bone, i.e. the first hit on the surface is the vertex itself.
+#              For "invisible" vertices, find the nearest "visible" vertices along surface by surface geodesic distance, and then go interior to the bone.
+# RigNet Copyright 2020 University of Massachusetts
+# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License.
+# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet.
+#-------------------------------------------------------------------------------
+
+import sys
+sys.path.append("./")
+import os
+import trimesh
+import numpy as np
+import open3d as o3d
+from utils.os_utils import mkdir_p
+from utils.rig_parser import Info
+from geometric_proc.common_ops import get_bones, calc_surface_geodesic
+
+
+def pts2line(pts, lines):
+    '''
+    Calculate points-to-bone distance. Point to line segment distance refer to
+    https://stackoverflow.com/questions/849211/shortest-distance-between-a-point-and-a-line-segment
+    :param pts: N*3
+    :param lines: N*6, where [N,0:3] is the starting position and [N, 3:6] is the ending position
+    :return: origins are the neatest projected position of the point on the line.
+             ends are the points themselves.
+             dist is the distance in between, which is the distance from points to lines.
+             Origins and ends will be used for generate rays.
+    '''
+    l2 = np.sum((lines[:, 3:6] - lines[:, 0:3]) ** 2, axis=1)
+    origins = np.zeros((len(pts) * len(lines), 3))
+    ends = np.zeros((len(pts) * len(lines), 3))
+    dist = np.zeros((len(pts) * len(lines)))
+    for l in range(len(lines)):
+        if np.abs(l2[l]) < 1e-8:  # for zero-length edges
+            origins[l * len(pts):(l + 1) * len(pts)] = lines[l][0:3]
+        else:  # for other edges
+            t = np.sum((pts - lines[l][0:3][np.newaxis, :]) * (lines[l][3:6] - lines[l][0:3])[np.newaxis, :], axis=1) / \
+                l2[l]
+            t = np.clip(t, 0, 1)
+            t_pos = lines[l][0:3][np.newaxis, :] + t[:, np.newaxis] * (lines[l][3:6] - lines[l][0:3])[np.newaxis, :]
+            origins[l * len(pts):(l + 1) * len(pts)] = t_pos
+        ends[l * len(pts):(l + 1) * len(pts)] = pts
+        dist[l * len(pts):(l + 1) * len(pts)] = np.linalg.norm(
+            origins[l * len(pts):(l + 1) * len(pts)] - ends[l * len(pts):(l + 1) * len(pts)], axis=1)
+    return origins, ends, dist
+
+
+def calc_pts2bone_visible_mat(mesh, origins, ends):
+    '''
+    Check whether the surface point is visible by the internal bone.
+    Visible is defined as no occlusion on the path between.
+    :param mesh:
+    :param surface_pts: points on the surface (n*3)
+    :param origins: origins of rays
+    :param ends: ends of the rays, together with origins, we can decide the direction of the ray.
+    :return: binary visibility matrix (n*m), where 1 indicate the n-th surface point is visible to the m-th ray
+    '''
+    ray_dir = ends - origins
+    RayMeshIntersector = trimesh.ray.ray_triangle.RayMeshIntersector(mesh)
+    locations, index_ray, index_tri = RayMeshIntersector.intersects_location(origins, ray_dir + 1e-15)
+    locations_per_ray = [locations[index_ray == i] for i in range(len(ray_dir))]
+    min_hit_distance = []
+    for i in range(len(locations_per_ray)):
+        if len(locations_per_ray[i]) == 0:
+            min_hit_distance.append(np.linalg.norm(ray_dir[i]))
+        else:
+            min_hit_distance.append(np.min(np.linalg.norm(locations_per_ray[i] - origins[i], axis=1)))
+    min_hit_distance = np.array(min_hit_distance)
+    distance = np.linalg.norm(ray_dir, axis=1)
+    vis_mat = (np.abs(min_hit_distance - distance) < 1e-4)
+    return vis_mat
+
+
+def show_visible_mat(mesh_filename, joint_pos, vis_mat, joint_id):
+    from utils.vis_utils import drawSphere
+
+    mesh_o3d = o3d.io.read_triangle_mesh(mesh_filename)
+    mesh_trimesh = trimesh.load(mesh_filename)
+    visible = vis_mat[:, joint_id]
+
+    mesh_ls = o3d.geometry.LineSet.create_from_triangle_mesh(mesh_o3d)
+    mesh_ls.colors = o3d.utility.Vector3dVector([[0.8, 0.8, 0.8] for i in range(len(mesh_ls.lines))])
+    pcd = o3d.geometry.PointCloud()
+    pcd.points = o3d.utility.Vector3dVector(np.array(mesh_trimesh.vertices)[visible])
+    pcd.colors = o3d.utility.Vector3dVector(np.repeat(np.array([[0.0, 0.0, 1.0]]), int(np.sum(visible)), axis=0))
+    vis = o3d.visualization.Visualizer()
+    vis.create_window()
+    vis.add_geometry(mesh_ls)
+    vis.add_geometry(drawSphere(joint_pos[joint_id], 0.005, color=[1.0, 0.0, 0.0]))
+    vis.add_geometry(pcd)
+    vis.run()
+    vis.destroy_window()
+
+
+def one_process(dataset_folder, start_id, end_id):
+    model_list = np.loadtxt(os.path.join(dataset_folder, 'model_list.txt'), dtype=int)
+    model_list = model_list[start_id: end_id]
+    remesh_obj_folder = os.path.join(dataset_folder, "obj_remesh")
+    mkdir_p(os.path.join(dataset_folder, "volumetric_geodesic/"))
+    
+    for model_id in model_list:
+        print(model_id)
+        if os.path.exists(os.path.join(dataset_folder, "volumetric_geodesic/{:d}_volumetric_geo.npy".format(model_id))):
+            continue
+        remeshed_obj_filename = os.path.join(dataset_folder, 'obj_remesh/{:d}.obj'.format(model_id))
+        ori_obj_filename = os.path.join(dataset_folder, 'obj/{:d}.obj'.format(model_id))
+        info_filename = os.path.join(dataset_folder, 'rig_info/{:d}.txt'.format(model_id))
+        
+        pts = np.array(o3d.io.read_triangle_mesh(os.path.join(remesh_obj_folder, '{:d}.obj'.format(model_id))).vertices)
+
+        mesh_remesh = trimesh.load(remeshed_obj_filename)
+        mesh_ori = trimesh.load(ori_obj_filename)
+        rig_info = Info(info_filename)
+        bones, bone_name, _ = get_bones(rig_info)
+        origins, ends, pts_bone_dist = pts2line(pts, bones)
+
+        if os.path.exists(os.path.join(dataset_folder, "volumetric_geodesic/{:d}_visibility_raw.npy".format(model_id))):
+            pts_bone_visibility = np.load(os.path.join(dataset_folder, "volumetric_geodesic/{:d}_visibility_raw.npy".format(model_id)))
+        else:
+            # pick one mesh with fewer faces to speed up
+            if len(mesh_remesh.faces) < len(mesh_ori.faces):
+                trimesh.repair.fix_normals(mesh_remesh)
+                pts_bone_visibility = calc_pts2bone_visible_mat(mesh_remesh, origins, ends)
+            else:
+                trimesh.repair.fix_normals(mesh_ori)
+                pts_bone_visibility = calc_pts2bone_visible_mat(mesh_ori, origins, ends)
+            pts_bone_visibility = pts_bone_visibility.reshape(len(bones), len(pts)).transpose()
+            #np.save(os.path.join(dataset_folder, "volumetric_geodesic/{:d}_visibility_raw.npy".format(model_id)), pts_bone_visibility)
+        pts_bone_dist = pts_bone_dist.reshape(len(bones), len(pts)).transpose()
+
+        # remove visible points which are too far
+        if os.path.exists(os.path.join(dataset_folder, "volumetric_geodesic/{:d}_visibility_filtered.npy".format(model_id))):
+            pts_bone_visibility = np.load(os.path.join(dataset_folder, "volumetric_geodesic/{:d}_visibility_filtered.npy".format(model_id)))
+        else:
+            for b in range(pts_bone_visibility.shape[1]):
+                visible_pts = np.argwhere(pts_bone_visibility[:, b] == 1).squeeze(1)
+                if len(visible_pts) == 0:
+                   continue
+                threshold_b = np.percentile(pts_bone_dist[visible_pts, b], 15)
+                pts_bone_visibility[pts_bone_dist[:, b] > 1.3 * threshold_b, b] = False
+            #np.save(os.path.join(dataset_folder, "volumetric_geodesic/{:d}_visibility_filtered.npy".format(model_id)), pts_bone_visibility)
+
+        mesh = o3d.io.read_triangle_mesh(os.path.join(remesh_obj_folder, '{:d}.obj'.format(model_id)))
+        surface_geodesic = calc_surface_geodesic(mesh)
+
+        visible_matrix = np.zeros(pts_bone_visibility.shape)
+        visible_matrix[np.where(pts_bone_visibility == 1)] = pts_bone_dist[np.where(pts_bone_visibility == 1)]
+        euc_dist = np.sqrt(np.sum((pts[np.newaxis, ...] - pts[:, np.newaxis, :]) ** 2, axis=2))
+        for c in range(visible_matrix.shape[1]):
+            unvisible_pts = np.argwhere(pts_bone_visibility[:, c] == 0).squeeze(1)
+            visible_pts = np.argwhere(pts_bone_visibility[:, c] == 1).squeeze(1)
+            if len(visible_pts) == 0:
+                visible_matrix[:, c] = pts_bone_dist[:, c]
+                continue
+            for r in unvisible_pts:
+                dist1 = np.min(surface_geodesic[r, visible_pts])
+                nn_visible = visible_pts[np.argmin(surface_geodesic[r, visible_pts])]
+                if np.isinf(dist1):
+                    visible_matrix[r, c] = 8.0 + pts_bone_dist[r, c]
+                else:
+                    visible_matrix[r, c] = dist1 + visible_matrix[nn_visible, c]
+        np.save(os.path.join(dataset_folder, "volumetric_geodesic/{:d}_volumetric_geo.npy".format(model_id)), visible_matrix)
+
+
+if __name__ == '__main__':
+    start_id = int(sys.argv[1])
+    end_id = int(sys.argv[2])
+    dataset_folder = "/media/zhanxu/4T1/ModelResource_Dataset/"
+    one_process(dataset_folder, start_id, end_id)

RigNet/maya_save_fbx.py

@@ -0,0 +1,100 @@
+#-------------------------------------------------------------------------------
+# Name:        maya_save_fbx.py
+# Purpose:     run this scriptin maya. Assemble predicted rig (.txt) and obj 
+#              mesh together to a FBX file
+# RigNet Copyright 2020 University of Massachusetts
+# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License.
+# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet.
+#-------------------------------------------------------------------------------
+import maya.OpenMaya as om
+import maya.cmds as cmds
+import maya.mel as mel
+import numpy as np
+import pymel.core as pm
+import os
+import glob
+
+
+def loadInfo(info_name, geo_name):
+    f_info = open(info_name,'r')
+    joint_pos = {}
+    joint_hier = {}
+    joint_skin = []
+    for line in f_info:
+        word = line.split()
+        if word[0] == 'joints':
+            joint_pos[word[1]] = [float(word[2]),  float(word[3]), float(word[4])]
+        if word[0] == 'root':
+            root_pos = joint_pos[word[1]]
+            root_name = word[1]
+            cmds.joint(p=(root_pos[0], root_pos[1],root_pos[2]), name = root_name)
+        if word[0] == 'hier':
+            if word[1] not in joint_hier.keys():
+                joint_hier[word[1]] = [word[2]]
+            else:
+                joint_hier[word[1]].append(word[2])
+        if word[0] == 'skin':
+            skin_item = word[1:]
+            joint_skin.append(skin_item)
+    f_info.close()
+    
+    this_level = [root_name]
+    while this_level:
+        next_level = []
+        for p_node in this_level:
+            if p_node in joint_hier.keys():
+                for c_node in joint_hier[p_node]:
+                    cmds.select(p_node, r=True)
+                    child_pos = joint_pos[c_node]
+                    cmds.joint(p=(child_pos[0], child_pos[1],child_pos[2]), name = c_node)
+                    next_level.append(c_node)
+        this_level = next_level         
+    cmds.joint(root_name, e=True, oj='xyz', sao='yup', ch=True, zso=True)
+    cmds.skinCluster( root_name, geo_name)
+    #print len(joint_skin)
+    for i in range(len(joint_skin)):
+        vtx_name = geo_name + '.vtx['+joint_skin[i][0]+']'
+        transValue = []
+        for j in range(1,len(joint_skin[i]),2):
+            transValue_item = (joint_skin[i][j], float(joint_skin[i][j+1]))
+            transValue.append(transValue_item) 
+        #print vtx_name, transValue
+        cmds.skinPercent( 'skinCluster1', vtx_name, transformValue=transValue)
+    cmds.skinPercent( 'skinCluster1', geo_name, pruneWeights=0.01, normalize=False )
+    return root_name, joint_pos
+
+
+def getGeometryGroups():
+    geo_list = []
+    geometries = cmds.ls(type='surfaceShape')
+    for geo in geometries:
+        if 'ShapeOrig' in geo:
+            '''
+            we can also use cmds.ls(geo, l=True)[0].split("|")[0]
+            to get the upper level node name, but stick on this way for now
+            '''
+            geo_name = geo.replace('ShapeOrig', '')
+            geo_list.append(geo_name)
+    if not geo_list:
+        geo_list = cmds.ls(type='surfaceShape')
+    return geo_list
+    
+    
+if __name__ == '__main__':
+    #model_id = "17872"
+    model_id = "smith"
+    print(model_id)
+    obj_name = 'D:\\{:s}_ori.obj'.format(model_id)
+    info_name = 'D:\\{:s}_ori_rig.txt'.format(model_id)
+    out_name = 'D:\\{:s}.fbx'.format(model_id)
+       
+    # import obj
+    cmds.file(new=True,force=True)
+    cmds.file(obj_name, o=True)
+
+    # import info
+    geo_list = getGeometryGroups()
+    root_name, _ = loadInfo(info_name, geo_list[0])
+    
+    # export fbx
+    pm.mel.FBXExport(f=out_name)

RigNet/models/GCN.py

@@ -0,0 +1,70 @@
+#-------------------------------------------------------------------------------
+# Name:        GCN.py
+# Purpose:     definition of joint prediction module.
+# RigNet Copyright 2020 University of Massachusetts
+# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License.
+# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet.
+#-------------------------------------------------------------------------------
+import torch
+from models.gcn_basic_modules import MLP, GCU
+from torch_scatter import scatter_max, scatter_mean
+from torch.nn import Sequential, Dropout, Linear, ReLU, Parameter
+
+
+class JointPredNet(torch.nn.Module):
+    def __init__(self, out_channels, input_normal, arch, aggr='max'):
+        super(JointPredNet, self).__init__()
+        self.input_normal = input_normal
+        self.arch = arch
+        if self.input_normal:
+            self.input_channel = 6
+        else:
+            self.input_channel = 3
+        self.gcu_1 = GCU(in_channels=self.input_channel, out_channels=64, aggr=aggr)
+        self.gcu_2 = GCU(in_channels=64, out_channels=256, aggr=aggr)
+        self.gcu_3 = GCU(in_channels=256, out_channels=512, aggr=aggr)
+        # feature compression
+        self.mlp_glb = MLP([(64 + 256 + 512), 1024])
+        self.mlp_tramsform = Sequential(MLP([1024 + self.input_channel + 64 + 256 +512, 1024, 256]),
+                                        Dropout(0.7), Linear(256, out_channels))
+        if self.arch == 'jointnet':
+            torch.nn.init.zeros_(self.mlp_tramsform[2].weight)
+            torch.nn.init.zeros_(self.mlp_tramsform[2].bias)
+
+    def forward(self, data):
+        if self.input_normal:
+            x = torch.cat([data.pos, data.x], dim=1)
+        else:
+            x = data.pos
+        geo_edge_index, tpl_edge_index, batch = data.geo_edge_index, data.tpl_edge_index, data.batch
+
+        x_1 = self.gcu_1(x, tpl_edge_index, geo_edge_index)
+        x_2 = self.gcu_2(x_1, tpl_edge_index, geo_edge_index)
+        x_3 = self.gcu_3(x_2, tpl_edge_index, geo_edge_index)
+        x_4 = self.mlp_glb(torch.cat([x_1, x_2, x_3], dim=1))
+
+        x_global, _ = scatter_max(x_4, data.batch, dim=0)
+        #x_global_mean = scatter_mean(x_4, data.batch, dim=0)
+        #x_global = torch.cat([x_global_max, x_global_mean], dim=1)
+        x_global = torch.repeat_interleave(x_global, torch.bincount(data.batch), dim=0)
+
+        x_5 = torch.cat([x_global, x, x_1, x_2, x_3], dim=1)
+        out = self.mlp_tramsform(x_5)
+        if self.arch == 'jointnet':
+            out = torch.tanh(out)
+        return out
+
+
+class JOINTNET_MASKNET_MEANSHIFT(torch.nn.Module):
+    def __init__(self):
+        super(JOINTNET_MASKNET_MEANSHIFT, self).__init__()
+        self.jointnet = JointPredNet(3, input_normal=False, arch='jointnet', aggr='max')
+        self.masknet = JointPredNet(1, input_normal=False, arch='masknet', aggr='max')
+        self.bandwidth = Parameter(torch.Tensor(1))
+        self.bandwidth.data.fill_(0.04)
+
+    def forward(self, data):
+        x_offset = self.jointnet(data)
+        x_mask_prob_0 = self.masknet(data)
+        x_mask_prob = torch.sigmoid(x_mask_prob_0)
+        return x_offset, x_mask_prob_0, x_mask_prob, self.bandwidth

RigNet/models/PairCls_GCN.py

@@ -0,0 +1,127 @@
+#-------------------------------------------------------------------------------
+# Name:        PairCls_GCN.py
+# Purpose:     definition of connectivity prediction module.
+# RigNet Copyright 2020 University of Massachusetts
+# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License.
+# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet.
+#-------------------------------------------------------------------------------
+import numpy as np
+import torch
+from models.gcn_basic_modules import MLP, GCU
+from torch.nn import Sequential, Dropout, Linear
+from torch_scatter import scatter_max
+from torch_geometric.nn import PointConv, fps, radius, global_max_pool, knn_interpolate
+
+
+class SAModule(torch.nn.Module):
+    def __init__(self, ratio, r, nn):
+        super(SAModule, self).__init__()
+        self.ratio = ratio
+        self.r = r
+        self.conv = PointConv(nn)
+
+    def forward(self, x, pos, batch):
+        idx = fps(pos, batch, ratio=self.ratio)
+        row, col = radius(pos, pos[idx], self.r, batch, batch[idx],
+                          max_num_neighbors=64)
+        edge_index = torch.stack([col, row], dim=0)
+        x = self.conv(x, (pos, pos[idx]), edge_index)
+        pos, batch = pos[idx], batch[idx]
+        return x, pos, batch
+
+
+class GlobalSAModule(torch.nn.Module):
+    def __init__(self, nn):
+        super(GlobalSAModule, self).__init__()
+        self.nn = nn
+
+    def forward(self, x, pos, batch):
+        x = self.nn(torch.cat([x, pos], dim=1))
+        x = global_max_pool(x, batch)
+        pos = pos.new_zeros((x.size(0), 3))
+        batch = torch.arange(x.size(0), device=batch.device)
+        return x, pos, batch
+
+
+class FPModule(torch.nn.Module):
+    def __init__(self, k, nn):
+        super(FPModule, self).__init__()
+        self.k = k
+        self.nn = nn
+
+    def forward(self, x, pos, batch, x_skip, pos_skip, batch_skip):
+        x = knn_interpolate(x, pos, pos_skip, batch, batch_skip, k=self.k)
+        if x_skip is not None:
+            x = torch.cat([x, x_skip], dim=1)
+        x = self.nn(x)
+        return x, pos_skip, batch_skip
+
+
+class ShapeEncoder(torch.nn.Module):
+    def __init__(self, aggr='max'):
+        super(ShapeEncoder, self).__init__()
+        self.gcu_1 = GCU(in_channels=3, out_channels=64, aggr=aggr)
+        self.gcu_2 = GCU(in_channels=64, out_channels=128, aggr=aggr)
+        self.gcu_3 = GCU(in_channels=128, out_channels=256, aggr=aggr)
+        self.mlp_glb = MLP([(64 + 128 + 256), 256, 64])
+
+    def forward(self, data):
+        x_1 = self.gcu_1(data.pos, data.tpl_edge_index, data.geo_edge_index)
+        x_2 = self.gcu_2(x_1, data.tpl_edge_index, data.geo_edge_index)
+        x_3 = self.gcu_3(x_2, data.tpl_edge_index, data.geo_edge_index)
+        x_4 = self.mlp_glb(torch.cat([x_1, x_2, x_3], dim=1))
+        x_global_shape, _ = scatter_max(x_4, data.batch, dim=0)
+        return x_global_shape
+
+
+class JointEncoder(torch.nn.Module):
+    def __init__(self):
+        super(JointEncoder, self).__init__()
+        #self.mlp_1 = MLP([3, 64, 128, 1024])
+        #self.mlp_2 = MLP([1024, 256, 128])
+
+        self.sa1_module_joints = SAModule(0.999, 0.4, MLP([3, 64, 64, 128]))
+        self.sa2_module_joints = SAModule(0.33, 0.6, MLP([128 + 3, 128, 128, 256]))
+        self.sa3_module_joints = GlobalSAModule(MLP([256 + 3, 256, 256, 512, 256, 128]))
+
+    def forward(self, joints, joints_batch):
+        '''x1 = self.mlp_1(joints_norepeat)
+        x_glb, _ = scatter_max(x1, joints_batch, dim=0)
+        x_glb = self.mlp_2(x_glb)
+        return x_glb'''
+
+        sa0_joints = (None, joints, joints_batch)
+        sa1_joints = self.sa1_module_joints(*sa0_joints)
+        sa2_joints = self.sa2_module_joints(*sa1_joints)
+        sa3_joints = self.sa3_module_joints(*sa2_joints)
+        x_glb_joint = sa3_joints[0]
+        return x_glb_joint
+
+
+class PairCls(torch.nn.Module):
+    def __init__(self):
+        super(PairCls, self).__init__()
+        self.expand_joint_feature = Sequential(MLP([8, 32, 64, 128, 256])) 
+        self.shape_encoder = ShapeEncoder()
+        self.joint_encoder = JointEncoder()
+        input_concat_dim = 448
+        self.mix_transform = Sequential(MLP([input_concat_dim, 128, 64]), Dropout(0.7), Linear(64, 1))
+
+    def forward(self, data, permute_joints=True):
+        joint_feature = self.joint_encoder(data.joints, data.joints_batch)
+        joint_feature = torch.repeat_interleave(joint_feature, torch.bincount(data.pairs_batch), dim=0)
+        shape_feature = self.shape_encoder(data)
+        shape_feature = torch.repeat_interleave(shape_feature, torch.bincount(data.pairs_batch), dim=0)
+
+        if permute_joints:
+            rand_permute = (torch.rand(len(data.pairs))>=0.5).long().to(data.pairs.device)
+            joints_pair = torch.cat((data.joints[torch.gather(data.pairs, dim=1, index=rand_permute.unsqueeze(dim=1)).squeeze(dim=1).long()],
+                                     data.joints[torch.gather(data.pairs, dim=1, index=1-rand_permute.unsqueeze(dim=1)).squeeze(dim=1).long()],
+                                     data.pair_attr[:, :-1]), dim=1)
+        else:
+            joints_pair = torch.cat((data.joints[data.pairs[:,0].long()], data.joints[data.pairs[:,1].long()], data.pair_attr[:, :-1]), dim=1)
+        pair_feature = self.expand_joint_feature(joints_pair)
+        pair_feature = torch.cat((shape_feature, joint_feature, pair_feature), dim=1)
+        pre_label = self.mix_transform(pair_feature)
+        gt_label = data.pair_attr[:, -1].unsqueeze(1)
+        return pre_label, gt_label

RigNet/models/ROOT_GCN.py

@@ -0,0 +1,136 @@
+#-------------------------------------------------------------------------------
+# Name:        Root_GCN.py
+# Purpose:     definition of root prediction module.
+# RigNet Copyright 2020 University of Massachusetts
+# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License.
+# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet.
+#-------------------------------------------------------------------------------
+import torch
+from models.gcn_basic_modules import MLP, GCU
+from torch_scatter import scatter_max
+from torch.nn import Sequential, Linear
+from torch_geometric.nn import PointConv, fps, radius, global_max_pool, knn_interpolate
+__all__ = ['ROOTNET']
+
+class SAModule(torch.nn.Module):
+    def __init__(self, ratio, r, nn):
+        super(SAModule, self).__init__()
+        self.ratio = ratio
+        self.r = r
+        self.conv = PointConv(nn)
+
+    def forward(self, x, pos, batch):
+        idx = fps(pos, batch, ratio=self.ratio)
+        row, col = radius(pos, pos[idx], self.r, batch, batch[idx],
+                          max_num_neighbors=64)
+        edge_index = torch.stack([col, row], dim=0)
+        x = self.conv(x, (pos, pos[idx]), edge_index)
+        pos, batch = pos[idx], batch[idx]
+        return x, pos, batch
+
+
+class GlobalSAModule(torch.nn.Module):
+    def __init__(self, nn):
+        super(GlobalSAModule, self).__init__()
+        self.nn = nn
+
+    def forward(self, x, pos, batch):
+        x = self.nn(torch.cat([x, pos], dim=1))
+        x = global_max_pool(x, batch)
+        pos = pos.new_zeros((x.size(0), 3))
+        batch = torch.arange(x.size(0), device=batch.device)
+        return x, pos, batch
+
+
+class FPModule(torch.nn.Module):
+    def __init__(self, k, nn):
+        super(FPModule, self).__init__()
+        self.k = k
+        self.nn = nn
+
+    def forward(self, x, pos, batch, x_skip, pos_skip, batch_skip):
+        x = knn_interpolate(x, pos, pos_skip, batch, batch_skip, k=self.k)
+        if x_skip is not None:
+            x = torch.cat([x, x_skip], dim=1)
+        x = self.nn(x)
+        return x, pos_skip, batch_skip
+
+
+class ShapeEncoder(torch.nn.Module):
+    def __init__(self, aggr='max'):
+        super(ShapeEncoder, self).__init__()
+        self.gcu_1 = GCU(in_channels=3, out_channels=64, aggr=aggr)
+        self.gcu_2 = GCU(in_channels=64, out_channels=128, aggr=aggr)
+        self.gcu_3 = GCU(in_channels=128, out_channels=256, aggr=aggr)
+        self.mlp_glb = MLP([(64 + 128 + 256), 128])
+        #self.mlp_glb = MLP([(64 + 128 + 256), 512])
+
+    def forward(self, data):
+        x_1 = self.gcu_1(data.pos, data.tpl_edge_index, data.geo_edge_index)
+        x_2 = self.gcu_2(x_1, data.tpl_edge_index, data.geo_edge_index)
+        x_3 = self.gcu_3(x_2, data.tpl_edge_index, data.geo_edge_index)
+        x_4 = self.mlp_glb(torch.cat([x_1, x_2, x_3], dim=1))
+        x_global, _ = scatter_max(x_4, data.batch, dim=0)
+        return x_global
+
+
+class JointEncoder(torch.nn.Module):
+    def __init__(self):
+        super(JointEncoder, self).__init__()
+        '''self.mlp_1 = MLP([4, 64])
+        self.mlp_2 = MLP([64, 128, 1024])
+        self.mlp_3 = MLP([1088, 512, 256, 128, 64])'''
+        self.sa1_joint = SAModule(0.999, 0.4, MLP([4, 64, 64, 128]))
+        self.sa2_joint = SAModule(0.33, 0.6, MLP([128 + 3, 128, 128, 256]))
+        self.sa3_joint = GlobalSAModule(MLP([256 + 3, 256, 256, 512]))
+        self.fp3_joint = FPModule(1, MLP([512 + 256, 256, 256]))
+        self.fp2_joint = FPModule(3, MLP([256 + 128, 128, 128]))
+        self.fp1_joint = FPModule(3, MLP([128 + 1, 128, 128]))
+
+    def forward(self, x, pos, batch):
+        '''x1= self.mlp_1(torch.cat((pos, x), dim=1))
+        x2 = self.mlp_2(x1)
+        x_glb, _ = scatter_max(x2, batch, dim=0)
+        x_glb = torch.repeat_interleave(x_glb, torch.bincount(batch), dim=0)
+        x3 = self.mlp_3(torch.cat((x_glb, x1), dim=1))
+        return x3'''
+        sa0_joint = (x, pos, batch)
+        sa1_joint = self.sa1_joint(*sa0_joint)
+        sa2_joint = self.sa2_joint(*sa1_joint)
+        sa3_joint = self.sa3_joint(*sa2_joint)
+        fp3_joint = self.fp3_joint(*sa3_joint, *sa2_joint)
+        fp2_joint = self.fp2_joint(*fp3_joint, *sa1_joint)
+        x_joint, _, _ = self.fp1_joint(*fp2_joint, *sa0_joint)
+        return x_joint
+
+
+class ROOTNET(torch.nn.Module):
+    def __init__(self):
+        super(ROOTNET, self).__init__()
+        self.shape_encoder = ShapeEncoder()
+        self.joint_encoder = JointEncoder()
+        self.back_layers = Sequential(MLP([128 + 128, 200, 64]), Linear(64, 1))
+
+    def forward(self, data, shuffle=True):
+        joints_label = []
+        joints_shuffle = []
+        for i in range(len(torch.unique(data.joints_batch))):
+            joint_i = data.joints[data.joints_batch==i]
+            label_i = joint_i.new(torch.Size((joint_i.shape[0], 1))).zero_()
+            label_i[0, 0] = 1
+            # random shuffle
+            if shuffle:
+                idx = torch.randperm(label_i.nelement())
+                label_i = label_i[idx]
+                joint_i = joint_i[idx]
+            joints_shuffle.append(joint_i)
+            joints_label.append(label_i)
+        joints_shuffle = torch.cat(joints_shuffle, dim=0)
+        joints_label = torch.cat(joints_label)
+
+        x_glb_shape = self.shape_encoder(data)
+        shape_feature = torch.repeat_interleave(x_glb_shape, torch.bincount(data.joints_batch), dim=0)
+        joint_feature = self.joint_encoder(torch.abs(joints_shuffle[:,0:1]), joints_shuffle, data.joints_batch)
+        x_joint = torch.cat([shape_feature, joint_feature], dim=1)
+        x_joint = self.back_layers(x_joint)
+        return x_joint, joints_label

RigNet/models/SKINNING.py

@@ -0,0 +1,74 @@
+#-------------------------------------------------------------------------------
+# Name:        SKINNING.py
+# Purpose:     definition of skinning prediction module.
+# RigNet Copyright 2020 University of Massachusetts
+# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License.
+# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet.
+#-------------------------------------------------------------------------------
+import numpy as np
+import torch
+from torch_scatter import scatter_max
+from torch.nn import Sequential as Seq, Linear as Lin, ReLU, BatchNorm1d as BN, Dropout
+from models.gcn_basic_modules import GCU, MLP
+
+__all__ = ['SKINNET', 'skinnet']
+
+
+class SKINNET(torch.nn.Module):
+    def __init__(self, nearest_bone, use_Dg, use_Lf, aggr='max'):
+        super(SKINNET, self).__init__()
+        self.num_nearest_bone = nearest_bone
+        self.use_Dg = use_Dg
+        self.use_Lf = use_Lf
+        if self.use_Dg and self.use_Lf:
+            input_dim = 3 + self.num_nearest_bone * 8
+        elif self.use_Dg and not self.use_Lf:
+            input_dim = 3 + self.num_nearest_bone * 7
+        elif self.use_Lf and not self.use_Dg:
+            input_dim = 3 + self.num_nearest_bone * 7
+        else:
+            input_dim = 3 + self.num_nearest_bone * 6
+        self.multi_layer_tranform1 = MLP([input_dim, 128, 64])
+        self.gcu1 = GCU(in_channels=64, out_channels=512, aggr=aggr)
+        self.gcu2 = GCU(in_channels=512, out_channels=256, aggr=aggr)
+        self.gcu3 = GCU(in_channels=256, out_channels=256, aggr=aggr)
+        self.multi_layer_tranform2 = MLP([512, 512, 1024])
+
+        self.cls_branch = Seq(Lin(1024 + 256, 1024), ReLU(), BN(1024), Lin(1024, 512), ReLU(), BN(512),
+                              Lin(512, self.num_nearest_bone))
+
+    def forward(self, data):
+        samples = data.skin_input
+        if self.use_Dg and self.use_Lf:
+            samples = samples[:, 0: 8 * self.num_nearest_bone]
+        elif self.use_Dg and not self.use_Lf:
+            samples = samples[:, np.arange(samples.shape[1]) % 8 != 7]
+            samples = samples[:, 0: 7 * self.num_nearest_bone]
+        elif self.use_Lf and not self.use_Dg:
+            samples = samples[:, np.arange(samples.shape[1]) % 8 != 6]
+            samples = samples[:, 0: 7 * self.num_nearest_bone]
+        else:
+            samples = samples[:, np.arange(samples.shape[1]) % 8 != 7]
+            samples = samples[:, np.arange(samples.shape[1]) % 7 != 6]
+            samples = samples[:, 0: 6 * self.num_nearest_bone]
+
+        raw_input = torch.cat([data.pos, samples], dim=1)
+
+        x_0 = self.multi_layer_tranform1(raw_input)
+        x_1 = self.gcu1(x_0, data.tpl_edge_index, data.geo_edge_index)
+
+        x_global = self.multi_layer_tranform2(x_1)
+        x_global, _ = scatter_max(x_global, data.batch, dim=0)
+
+        x_2 = self.gcu2(x_1, data.tpl_edge_index, data.geo_edge_index)
+        x_3 = self.gcu3(x_2, data.tpl_edge_index, data.geo_edge_index)
+        x_global = torch.repeat_interleave(x_global, torch.bincount(data.batch), dim=0)
+        x_4 = torch.cat([x_3, x_global], dim=1)
+
+        skin_cls_pred = self.cls_branch(x_4)
+        return skin_cls_pred
+
+
+def skinnet(nearest_bone, use_Dg, use_Lf):
+    model = SKINNET(nearest_bone=nearest_bone, use_Dg=use_Dg, use_Lf=use_Lf)
+    return model

RigNet/models/__init__.py

@@ -0,0 +1,4 @@
+from .GCN import *
+from .PairCls_GCN import PairCls
+from .ROOT_GCN import ROOTNET
+from .SKINNING import *

RigNet/models/gcn_basic_modules.py

@@ -0,0 +1,62 @@
+#-------------------------------------------------------------------------------
+# Name:        gcn_basic_modules.py
+# Purpose:     basic structures (layers) used in our models
+# RigNet Copyright 2020 University of Massachusetts
+# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License.
+# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet.
+#-------------------------------------------------------------------------------
+import torch
+from torch_geometric.nn import MessagePassing
+from torch_scatter import scatter_max, scatter_mean
+from torch_geometric.utils import add_self_loops, remove_self_loops, softmax
+from torch.nn import Sequential, Dropout, Linear, ReLU, BatchNorm1d, Parameter
+
+
+def MLP(channels, batch_norm=True):
+    if batch_norm:
+        return Sequential(*[Sequential(Linear(channels[i - 1], channels[i]), ReLU(), BatchNorm1d(channels[i], momentum=0.1))
+                            for i in range(1, len(channels))])
+    else:
+        return Sequential(*[Sequential(Linear(channels[i - 1], channels[i]), ReLU()) for i in range(1, len(channels))])
+
+
+class EdgeConv(MessagePassing):
+    def __init__(self, in_channels, out_channels, nn, aggr='max', **kwargs):
+        super(EdgeConv, self).__init__(aggr=aggr, **kwargs)
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.nn = nn
+
+    def forward(self, x, edge_index):
+        """"""
+        x = x.unsqueeze(-1) if x.dim() == 1 else x
+        edge_index, _ = remove_self_loops(edge_index)
+        edge_index, _ = add_self_loops(edge_index, num_nodes=x.size(0))
+        return self.propagate(edge_index, x=x)
+
+    def message(self, x_i, x_j):
+        return self.nn(torch.cat([x_i, (x_j - x_i)], dim=1))
+
+    def update(self, aggr_out):
+        aggr_out = aggr_out.view(-1, self.out_channels)
+        return aggr_out
+
+    def __repr__(self):
+        return '{}(nn={})'.format(self.__class__.__name__, self.nn)
+
+
+class GCU(torch.nn.Module):
+    def __init__(self, in_channels, out_channels, aggr='max'):
+        super(GCU, self).__init__()
+        self.edge_conv_tpl = EdgeConv(in_channels=in_channels, out_channels=out_channels // 2,
+                                      nn=MLP([in_channels * 2, out_channels // 2, out_channels // 2]), aggr=aggr)
+        self.edge_conv_geo = EdgeConv(in_channels=in_channels, out_channels=out_channels // 2,
+                                  nn=MLP([in_channels * 2, out_channels // 2, out_channels // 2]), aggr=aggr)
+        self.mlp = MLP([out_channels, out_channels])
+
+    def forward(self, x, tpl_edge_index, geo_edge_index):
+        x_tpl = self.edge_conv_tpl(x, tpl_edge_index)
+        x_geo = self.edge_conv_geo(x, geo_edge_index)
+        x_out = torch.cat([x_tpl, x_geo], dim=1)
+        x_out = self.mlp(x_out)
+        return x_out

RigNet/models/supplemental_layers/cross_entropy_with_probs.py

@@ -0,0 +1,67 @@
+import torch
+import torch.nn.functional as F
+
+
+def cross_entropy_with_probs_snorkel(input, target, weight=None, reduction="mean"):
+    """Calculate cross-entropy loss when targets are probabilities (floats), not ints.
+    PyTorch's F.cross_entropy() method requires integer labels; it does accept
+    probabilistic labels. We can, however, simulate such functionality with a for loop,
+    calculating the loss contributed by each class and accumulating the results.
+    Libraries such as keras do not require this workaround, as methods like
+    "categorical_crossentropy" accept float labels natively.
+    Note that the method signature is intentionally very similar to F.cross_entropy()
+    so that it can be used as a drop-in replacement when target labels are changed from
+    from a 1D tensor of ints to a 2D tensor of probabilities.
+    Parameters
+    ----------
+    input
+        A [num_points, num_classes] tensor of logits
+    target
+        A [num_points, num_classes] tensor of probabilistic target labels
+    weight
+        An optional [num_classes] array of weights to multiply the loss by per class
+    reduction
+        One of "none", "mean", "sum", indicating whether to return one loss per data
+        point, the mean loss, or the sum of losses
+    Returns
+    -------
+    torch.Tensor
+        The calculated loss
+    Raises
+    ------
+    ValueError
+        If an invalid reduction keyword is submitted
+    """
+    num_points, num_classes = input.shape
+    # Note that t.new_zeros, t.new_full put tensor on same device as t
+    cum_losses = input.new_zeros(num_points)
+    for y in range(num_classes):
+        target_temp = input.new_full((num_points,), y, dtype=torch.long)
+        y_loss = F.cross_entropy(input, target_temp, reduction="none")
+        if weight is not None:
+            y_loss = y_loss * weight[y]
+        cum_losses += target[:, y].float() * y_loss
+
+    if reduction == "none":
+        return cum_losses
+    elif reduction == "mean":
+        return cum_losses.mean()
+    elif reduction == "sum":
+        return cum_losses.sum()
+    else:
+        raise ValueError("Keyword 'reduction' must be one of ['none', 'mean', 'sum']")
+
+
+def cross_entropy_with_probs(input, target, weight=None, reduction="mean"):
+    input_logsoftmax = F.log_softmax(input, dim=1)
+    cum_losses = -target * input_logsoftmax
+    if weight is not None:
+        cum_losses = cum_losses * weight
+    if reduction == "none":
+        return cum_losses
+    elif reduction == "mean":
+        return cum_losses.sum(dim=1).mean()
+    elif reduction == "sum":
+        return cum_losses.sum()
+    else:
+        raise ValueError("Keyword 'reduction' must be one of ['none', 'mean', 'sum']")

RigNet/models/supplemental_layers/pytorch_chamfer_dist.py

@@ -0,0 +1,26 @@
+import torch
+from torch_scatter import scatter_mean
+
+
+def chamfer_distance_with_average(p1, p2):
+
+    '''
+    Calculate Chamfer Distance between two point sets
+    :param p1: size[1, N, D]
+    :param p2: size[1, M, D]
+    :param debug: whether need to output debug info
+    :return: sum of Chamfer Distance of two point sets
+    '''
+
+    assert p1.size(0) == 1 and p2.size(0) == 1
+    assert p1.size(2) == p2.size(2)
+    p1 = p1.repeat(p2.size(1), 1, 1)
+    p1 = p1.transpose(0, 1)
+    p2 = p2.repeat(p1.size(0), 1, 1)
+    dist = torch.add(p1, torch.neg(p2))
+    dist_norm = torch.norm(dist, 2, dim=2)
+    dist1 = torch.min(dist_norm, dim=1)[0]
+    dist2 = torch.min(dist_norm, dim=0)[0]
+    loss = 0.5 * ((torch.mean(dist1)) + (torch.mean(dist2)))
+    return loss
+

RigNet/mst_generate.py

@@ -0,0 +1,225 @@
+#-------------------------------------------------------------------------------
+# Name:        mst_generate.py
+# Purpose:     Generate skeleton as a tree based on predicted joints.
+# RigNet Copyright 2020 University of Massachusetts
+# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License.
+# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet.
+#-------------------------------------------------------------------------------
+
+import os
+import cv2
+import argparse
+import numpy as np
+import open3d as o3d
+from utils import binvox_rw
+from utils.tree_utils import TreeNode
+from utils.rig_parser import Skel
+from utils.vis_utils import show_obj_skel, draw_shifted_pts
+from utils.io_utils import readPly
+from utils.cluster_utils import meanshift_cluster, nms_meanshift
+from utils.mst_utils import primMST_symmetry, loadSkel_recur, increase_cost_for_outside_bone, flip, inside_check, sample_on_bone
+from gen_dataset import get_geo_edges, get_tpl_edges
+from geometric_proc.common_ops import calc_surface_geodesic
+
+import torch
+from torch_geometric.data import Data
+from torch_geometric.utils import add_self_loops
+
+from models.ROOT_GCN import ROOTNET
+from models.PairCls_GCN import PairCls
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+
+def predict_joints(model_id, args):
+    """
+    predict joints for a specified model
+    :param model_id: processed model ID number
+    :param args:
+    :return: predicted joints, and voxelized mesh
+    """
+    vox_folder = os.path.join(args.dataset_folder, 'vox/')
+    mesh_folder = os.path.join(args.dataset_folder, 'obj_remesh/')
+    raw_pred = os.path.join(args.res_folder, '{:d}.ply'.format(model_id))
+    vox_file = os.path.join(vox_folder, '{:d}.binvox'.format(model_id))
+    mesh_file = os.path.join(mesh_folder, '{:d}.obj'.format(model_id))
+    pred_attn = np.load(os.path.join(args.res_folder, '{:d}_attn.npy'.format(model_id)))
+
+    with open(vox_file, 'rb') as fvox:
+        vox = binvox_rw.read_as_3d_array(fvox)
+    pred_joints = readPly(raw_pred)
+    pred_joints, index_inside = inside_check(pred_joints, vox)
+    pred_attn = pred_attn[index_inside, :]
+    # img = draw_shifted_pts(mesh_file, pred_joints, weights=pred_attn)
+
+    bandwidth = np.load(os.path.join(args.res_folder, '{:d}_bandwidth.npy'.format(model_id)))
+    bandwidth = bandwidth[0]
+    pred_joints = pred_joints[pred_attn.squeeze() > 1e-3]
+    pred_attn = pred_attn[pred_attn.squeeze() > 1e-3]
+
+    # reflect raw points
+    pred_joints_reflect = pred_joints * np.array([[-1, 1, 1]])
+    pred_joints = np.concatenate((pred_joints, pred_joints_reflect), axis=0)
+    pred_attn = np.tile(pred_attn, (2, 1))
+    # img = draw_shifted_pts(mesh_file, pred_joints, weights=pred_attn)
+    # cv2.imwrite(os.path.join(res_folder, '{:s}_raw.jpg'.format(model_id)), img[:, :, ::-1])
+
+    pred_joints = meanshift_cluster(pred_joints, bandwidth, pred_attn, max_iter=20)
+    Y_dist = np.sum(((pred_joints[np.newaxis, ...] - pred_joints[:, np.newaxis, :]) ** 2), axis=2)
+    density = np.maximum(bandwidth ** 2 - Y_dist, np.zeros(Y_dist.shape))
+    # density = density * pred_attn
+    density = np.sum(density, axis=0)
+    density_sum = np.sum(density)
+    pred_joints_ = pred_joints[density / density_sum > args.threshold_best]
+    density_ = density[density / density_sum > args.threshold_best]
+    pred_joints_ = nms_meanshift(pred_joints_, density_, bandwidth)
+    pred_joints_, _ = flip(pred_joints_)
+
+    reduce_threshold = args.threshold_best
+    while len(pred_joints_) < 2 and reduce_threshold > 1e-7:
+        # print('reducing')
+        reduce_threshold = reduce_threshold / 1.3
+        pred_joints_ = pred_joints[density / density_sum >= reduce_threshold]
+        density_ = density[density / density_sum > reduce_threshold]
+        pred_joints_ = nms_meanshift(pred_joints_, density_, bandwidth)
+        pred_joints_, _ = flip(pred_joints_)
+    if reduce_threshold <= 1e-7:
+        pred_joints_ = nms_meanshift(pred_joints_, density, bandwidth)
+        pred_joints_, _ = flip(pred_joints_)
+
+    pred_joints = pred_joints_
+    # img = draw_shifted_pts(mesh_file, pred_joints)
+    # cv2.imwrite(os.path.join(res_folder, '{:d}_joint.jpg'.format(model_id)), img)
+    # np.save(os.path.join(res_folder, '{:d}_joint.npy'.format(model_id)), pred_joints)
+    return pred_joints, vox
+
+
+def getInitId(data, model):
+    """
+    predict root joint ID via rootnet
+    :param data:
+    :param model:
+    :return:
+    """
+    with torch.no_grad():
+        root_prob, _ = model(data, shuffle=False)
+        root_prob = torch.sigmoid(root_prob).data.cpu().numpy()
+    root_id = np.argmax(root_prob)
+    return root_id
+
+
+def create_single_data(mesh, vox, surface_geodesic, pred_joints):
+    """
+    create data used as input to networks, wrapped by Data structure in pytorch-gemetric library
+    :param mesh: input mesh loaded by open3d
+    :param vox: voxelized mesh
+    :param surface_geodesic: geodesic distance matrix of all vertices
+    :param pred_joints: predicted joints
+    :return: wrapped data structure
+    """
+    mesh_v = np.asarray(mesh.vertices)
+    mesh_vn = np.asarray(mesh.vertex_normals)
+    mesh_f = np.asarray(mesh.triangles)
+
+    # vertices
+    v = np.concatenate((mesh_v, mesh_vn), axis=1)
+    v = torch.from_numpy(v).float()
+
+    # topology edges
+    print("     gathering topological edges.")
+    tpl_e = get_tpl_edges(mesh_v, mesh_f).T
+    tpl_e = torch.from_numpy(tpl_e).long()
+    tpl_e, _ = add_self_loops(tpl_e, num_nodes=v.size(0))
+
+    # geodesic edges
+    print("     gathering geodesic edges.")
+    geo_e = get_geo_edges(surface_geodesic, mesh_v).T
+    geo_e = torch.from_numpy(geo_e).long()
+    geo_e, _ = add_self_loops(geo_e, num_nodes=v.size(0))
+
+    batch = np.zeros(len(v))
+    batch = torch.from_numpy(batch).long()
+
+    pair_all = []
+    for joint1_id in range(len(pred_joints)):
+        for joint2_id in range(joint1_id + 1, len(pred_joints)):
+            dist = np.linalg.norm(pred_joints[joint1_id] - pred_joints[joint2_id])
+            bone_samples = sample_on_bone(pred_joints[joint1_id], pred_joints[joint2_id])
+            bone_samples_inside, _ = inside_check(bone_samples, vox)
+            outside_proportion = len(bone_samples_inside) / (len(bone_samples) + 1e-10)
+            pair = np.array([joint1_id, joint2_id, dist, outside_proportion, 1])
+            pair_all.append(pair)
+    pair_all = np.array(pair_all)
+    pair_all = torch.from_numpy(pair_all).float()
+    num_pair = len(pair_all)
+    num_joint = len(pred_joints)
+    if len(pred_joints) < len(mesh_v):
+        pred_joints = np.tile(pred_joints, (round(1.0 * len(mesh_v) / len(pred_joints) + 0.5), 1))
+        pred_joints = pred_joints[:len(mesh_v), :]
+    elif len(pred_joints) > len(mesh_v):
+        pred_joints = pred_joints[:len(mesh_v), :]
+    pred_joints = torch.from_numpy(pred_joints).float()
+
+    data = Data(x=torch.from_numpy(mesh_vn), pos=torch.from_numpy(mesh_v).float(), batch=batch, y=pred_joints,
+                pairs=pair_all, num_pair=[num_pair], tpl_edge_index=tpl_e, geo_edge_index=geo_e, num_joint=[num_joint]).to(device)
+    return data
+
+
+def run_mst_generate(args):
+    """
+    generate skeleton in batch
+    :param args: input folder path and data folder path
+    """
+    test_list = np.loadtxt(os.path.join(args.dataset_folder, 'test_final.txt'), dtype=np.int)
+    root_select_model = ROOTNET()
+    root_select_model.to(device)
+    root_select_model.eval()
+    root_checkpoint = torch.load(args.rootnet)
+    root_select_model.load_state_dict(root_checkpoint['state_dict'])
+    connectivity_model = PairCls()
+    connectivity_model.to(device)
+    connectivity_model.eval()
+    conn_checkpoint = torch.load(args.bonenet)
+    connectivity_model.load_state_dict(conn_checkpoint['state_dict'])
+
+    for model_id in test_list:
+        print(model_id)
+        pred_joints, vox = predict_joints(model_id, args)
+        mesh_filename = os.path.join(args.dataset_folder, 'obj_remesh/{:d}.obj'.format(model_id))
+        mesh = o3d.io.read_triangle_mesh(mesh_filename)
+        surface_geodesic = calc_surface_geodesic(mesh)
+        data = create_single_data(mesh, vox, surface_geodesic, pred_joints)
+        root_id = getInitId(data, root_select_model)
+        with torch.no_grad():
+            cost_matrix, _ = connectivity_model.forward(data)
+            connect_prob = torch.sigmoid(cost_matrix)
+        pair_idx = data.pairs.long().data.cpu().numpy()
+        cost_matrix = np.zeros((data.num_joint[0], data.num_joint[0]))
+        cost_matrix[pair_idx[:, 0], pair_idx[:, 1]] = connect_prob.data.cpu().numpy().squeeze()
+        cost_matrix = cost_matrix + cost_matrix.transpose()
+        cost_matrix = -np.log(cost_matrix+1e-10)
+        #cost_matrix = flip_cost_matrix(pred_joints, cost_matrix)
+        cost_matrix = increase_cost_for_outside_bone(cost_matrix, pred_joints, vox)
+
+        skel = Skel()
+        parent, key, root_id = primMST_symmetry(cost_matrix, root_id, pred_joints)
+        for i in range(len(parent)):
+            if parent[i] == -1:
+                skel.root = TreeNode('root', tuple(pred_joints[i]))
+                break
+        loadSkel_recur(skel.root, i, None, pred_joints, parent)
+        img = show_obj_skel(mesh_filename, skel.root)
+        cv2.imwrite(os.path.join(args.res_folder, '{:d}_skel.jpg'.format(model_id)), img[:,:,::-1])
+        skel.save(os.path.join(args.res_folder, '{:d}_skel.txt'.format(model_id)))
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='')
+    parser.add_argument('--dataset_folder', default='/media/zhanxu/4T1/ModelResource_RigNetv1_preproccessed/', type=str)
+    parser.add_argument('--res_folder', default='results/gcn_meanshift/best_25/', type=str)
+    parser.add_argument('--rootnet', default='checkpoints/rootnet/model_best.pth.tar', type=str)
+    parser.add_argument('--bonenet', default='checkpoints/bonenet/model_best.pth.tar', type=str)
+    parser.add_argument('--threshold_best', default=1e-5, type=float)
+    args = parser.parse_args()
+    print(args)
+    run_mst_generate(args)

RigNet/quick_start.py

@@ -0,0 +1,476 @@
+# ---------------------------------------------------------------------------------------------------------
+# Name:        quick_start.py
+# Purpose:     An easy-to-use demo. Also serves as an interface of the pipeline.
+# RigNet Copyright 2020 University of Massachusetts
+# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License.
+# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet.
+# ---------------------------------------------------------------------------------------------------------
+
+import os
+from sys import platform
+import trimesh
+import numpy as np
+import open3d as o3d
+import itertools as it
+
+import torch
+from torch_geometric.data import Data
+from torch_geometric.utils import add_self_loops
+
+from utils import binvox_rw
+from utils.rig_parser import Skel, Info
+from utils.tree_utils import TreeNode
+from utils.io_utils import assemble_skel_skin
+from utils.vis_utils import draw_shifted_pts, show_obj_skel, show_mesh_vox
+from utils.cluster_utils import meanshift_cluster, nms_meanshift
+from utils.mst_utils import increase_cost_for_outside_bone, primMST_symmetry, loadSkel_recur, inside_check, flip
+
+from geometric_proc.common_ops import get_bones, calc_surface_geodesic
+from geometric_proc.compute_volumetric_geodesic import pts2line, calc_pts2bone_visible_mat
+
+from gen_dataset import get_tpl_edges, get_geo_edges
+from mst_generate import sample_on_bone, getInitId
+from run_skinning import post_filter
+
+from models.GCN import JOINTNET_MASKNET_MEANSHIFT as JOINTNET
+from models.ROOT_GCN import ROOTNET
+from models.PairCls_GCN import PairCls as BONENET
+from models.SKINNING import SKINNET
+
+
+def normalize_obj(mesh_v):
+    dims = [max(mesh_v[:, 0]) - min(mesh_v[:, 0]),
+            max(mesh_v[:, 1]) - min(mesh_v[:, 1]),
+            max(mesh_v[:, 2]) - min(mesh_v[:, 2])]
+    scale = 1.0 / max(dims)
+    pivot = np.array([(min(mesh_v[:, 0]) + max(mesh_v[:, 0])) / 2, min(mesh_v[:, 1]),
+                      (min(mesh_v[:, 2]) + max(mesh_v[:, 2])) / 2])
+    mesh_v[:, 0] -= pivot[0]
+    mesh_v[:, 1] -= pivot[1]
+    mesh_v[:, 2] -= pivot[2]
+    mesh_v *= scale
+    return mesh_v, pivot, scale
+
+
+def create_single_data(mesh_filaname):
+    """
+    create input data for the network. The data is wrapped by Data structure in pytorch-geometric library
+    :param mesh_filaname: name of the input mesh
+    :return: wrapped data, voxelized mesh, and geodesic distance matrix of all vertices
+    """
+    mesh = o3d.io.read_triangle_mesh(mesh_filaname)
+    mesh.compute_vertex_normals()
+    mesh_v = np.asarray(mesh.vertices)
+    mesh_vn = np.asarray(mesh.vertex_normals)
+    mesh_f = np.asarray(mesh.triangles)
+
+    mesh_v, translation_normalize, scale_normalize = normalize_obj(mesh_v)
+    mesh_normalized = o3d.geometry.TriangleMesh(vertices=o3d.utility.Vector3dVector(mesh_v), triangles=o3d.utility.Vector3iVector(mesh_f))
+    o3d.io.write_triangle_mesh(mesh_filename.replace("_remesh.obj", "_normalized.obj"), mesh_normalized)
+
+    # vertices
+    v = np.concatenate((mesh_v, mesh_vn), axis=1)
+    v = torch.from_numpy(v).float()
+
+    # topology edges
+    print("     gathering topological edges.")
+    tpl_e = get_tpl_edges(mesh_v, mesh_f).T
+    tpl_e = torch.from_numpy(tpl_e).long()
+    tpl_e, _ = add_self_loops(tpl_e, num_nodes=v.size(0))
+
+    # surface geodesic distance matrix
+    print("     calculating surface geodesic matrix.")
+    surface_geodesic = calc_surface_geodesic(mesh)
+
+    # geodesic edges
+    print("     gathering geodesic edges.")
+    geo_e = get_geo_edges(surface_geodesic, mesh_v).T
+    geo_e = torch.from_numpy(geo_e).long()
+    geo_e, _ = add_self_loops(geo_e, num_nodes=v.size(0))
+
+    # batch
+    batch = torch.zeros(len(v), dtype=torch.long)
+
+    # voxel
+    if not os.path.exists(mesh_filaname.replace('_remesh.obj', '_normalized.binvox')):
+        if platform == "linux" or platform == "linux2":
+            os.system("./binvox -d 88 -pb " + mesh_filaname.replace("_remesh.obj", "_normalized.obj"))
+        elif platform == "win32":
+            os.system("binvox.exe -d 88 " + mesh_filaname.replace("_remesh.obj", "_normalized.obj"))
+        else:
+            raise Exception('Sorry, we currently only support windows and linux.')
+
+    with open(mesh_filaname.replace('_remesh.obj', '_normalized.binvox'), 'rb') as fvox:
+        vox = binvox_rw.read_as_3d_array(fvox)
+
+    data = Data(x=v[:, 3:6], pos=v[:, 0:3], tpl_edge_index=tpl_e, geo_edge_index=geo_e, batch=batch)
+    return data, vox, surface_geodesic, translation_normalize, scale_normalize
+
+
+def predict_joints(input_data, vox, joint_pred_net, threshold, bandwidth=None, mesh_filename=None):
+    """
+    Predict joints
+    :param input_data: wrapped input data
+    :param vox: voxelized mesh
+    :param joint_pred_net: network for predicting joints
+    :param threshold: density threshold to filter out shifted points
+    :param bandwidth: bandwidth for meanshift clustering
+    :param mesh_filename: mesh filename for visualization
+    :return: wrapped data with predicted joints, pair-wise bone representation added.
+    """
+    data_displacement, _, attn_pred, bandwidth_pred = joint_pred_net(input_data)
+    y_pred = data_displacement + input_data.pos
+    y_pred_np = y_pred.data.cpu().numpy()
+    attn_pred_np = attn_pred.data.cpu().numpy()
+    y_pred_np, index_inside = inside_check(y_pred_np, vox)
+    attn_pred_np = attn_pred_np[index_inside, :]
+    y_pred_np = y_pred_np[attn_pred_np.squeeze() > 1e-3]
+    attn_pred_np = attn_pred_np[attn_pred_np.squeeze() > 1e-3]
+
+    # symmetrize points by reflecting
+    y_pred_np_reflect = y_pred_np * np.array([[-1, 1, 1]])
+    y_pred_np = np.concatenate((y_pred_np, y_pred_np_reflect), axis=0)
+    attn_pred_np = np.tile(attn_pred_np, (2, 1))
+
+    #img = draw_shifted_pts(mesh_filename, y_pred_np, weights=attn_pred_np)
+    if bandwidth is None:
+        bandwidth = bandwidth_pred.item()
+    y_pred_np = meanshift_cluster(y_pred_np, bandwidth, attn_pred_np, max_iter=40)
+    #img = draw_shifted_pts(mesh_filename, y_pred_np, weights=attn_pred_np)
+
+    Y_dist = np.sum(((y_pred_np[np.newaxis, ...] - y_pred_np[:, np.newaxis, :]) ** 2), axis=2)
+    density = np.maximum(bandwidth ** 2 - Y_dist, np.zeros(Y_dist.shape))
+    density = np.sum(density, axis=0)
+    density_sum = np.sum(density)
+    y_pred_np = y_pred_np[density / density_sum > threshold]
+    attn_pred_np = attn_pred_np[density / density_sum > threshold][:, 0]
+    density = density[density / density_sum > threshold]
+
+    #img = draw_shifted_pts(mesh_filename, y_pred_np, weights=attn_pred_np)
+    pred_joints = nms_meanshift(y_pred_np, density, bandwidth)
+    pred_joints, _ = flip(pred_joints)
+    #img = draw_shifted_pts(mesh_filename, pred_joints)
+
+    # prepare and add new data members
+    pairs = list(it.combinations(range(pred_joints.shape[0]), 2))
+    pair_attr = []
+    for pr in pairs:
+        dist = np.linalg.norm(pred_joints[pr[0]] - pred_joints[pr[1]])
+        bone_samples = sample_on_bone(pred_joints[pr[0]], pred_joints[pr[1]])
+        bone_samples_inside, _ = inside_check(bone_samples, vox)
+        outside_proportion = len(bone_samples_inside) / (len(bone_samples) + 1e-10)
+        attr = np.array([dist, outside_proportion, 1])
+        pair_attr.append(attr)
+    pairs = np.array(pairs)
+    pair_attr = np.array(pair_attr)
+    pairs = torch.from_numpy(pairs).float()
+    pair_attr = torch.from_numpy(pair_attr).float()
+    pred_joints = torch.from_numpy(pred_joints).float()
+    joints_batch = torch.zeros(len(pred_joints), dtype=torch.long)
+    pairs_batch = torch.zeros(len(pairs), dtype=torch.long)
+
+    input_data.joints = pred_joints
+    input_data.pairs = pairs
+    input_data.pair_attr = pair_attr
+    input_data.joints_batch = joints_batch
+    input_data.pairs_batch = pairs_batch
+    return input_data
+
+
+def predict_skeleton(input_data, vox, root_pred_net, bone_pred_net, mesh_filename):
+    """
+    Predict skeleton structure based on joints
+    :param input_data: wrapped data
+    :param vox: voxelized mesh
+    :param root_pred_net: network to predict root
+    :param bone_pred_net: network to predict pairwise connectivity cost
+    :param mesh_filename: meshfilename for debugging
+    :return: predicted skeleton structure
+    """
+    root_id = getInitId(input_data, root_pred_net)
+    pred_joints = input_data.joints.data.cpu().numpy()
+
+    with torch.no_grad():
+        connect_prob, _ = bone_pred_net(input_data, permute_joints=False)
+        connect_prob = torch.sigmoid(connect_prob)
+    pair_idx = input_data.pairs.long().data.cpu().numpy()
+    prob_matrix = np.zeros((len(input_data.joints), len(input_data.joints)))
+    prob_matrix[pair_idx[:, 0], pair_idx[:, 1]] = connect_prob.data.cpu().numpy().squeeze()
+    prob_matrix = prob_matrix + prob_matrix.transpose()
+    cost_matrix = -np.log(prob_matrix + 1e-10)
+    cost_matrix = increase_cost_for_outside_bone(cost_matrix, pred_joints, vox)
+
+    pred_skel = Info()
+    parent, key, root_id = primMST_symmetry(cost_matrix, root_id, pred_joints)
+    for i in range(len(parent)):
+        if parent[i] == -1:
+            pred_skel.root = TreeNode('root', tuple(pred_joints[i]))
+            break
+    loadSkel_recur(pred_skel.root, i, None, pred_joints, parent)
+    pred_skel.joint_pos = pred_skel.get_joint_dict()
+    #show_mesh_vox(mesh_filename, vox, pred_skel.root)
+    try:
+        img = show_obj_skel(mesh_filename, pred_skel.root)
+    except:
+        print("Visualization is not supported on headless servers. Please consider other headless rendering methods.")
+    return pred_skel
+
+
+def calc_geodesic_matrix(bones, mesh_v, surface_geodesic, mesh_filename, subsampling=False):
+    """
+    calculate volumetric geodesic distance from vertices to each bones
+    :param bones: B*6 numpy array where each row stores the starting and ending joint position of a bone
+    :param mesh_v: V*3 mesh vertices
+    :param surface_geodesic: geodesic distance matrix of all vertices
+    :param mesh_filename: mesh filename
+    :return: an approaximate volumetric geodesic distance matrix V*B, were (v,b) is the distance from vertex v to bone b
+    """
+
+    if subsampling:
+        mesh0 = o3d.io.read_triangle_mesh(mesh_filename)
+        mesh0 = mesh0.simplify_quadric_decimation(3000)
+        o3d.io.write_triangle_mesh(mesh_filename.replace(".obj", "_simplified.obj"), mesh0)
+        mesh_trimesh = trimesh.load(mesh_filename.replace(".obj", "_simplified.obj"))
+        subsamples_ids = np.random.choice(len(mesh_v), np.min((len(mesh_v), 1500)), replace=False)
+        subsamples = mesh_v[subsamples_ids, :]
+        surface_geodesic = surface_geodesic[subsamples_ids, :][:, subsamples_ids]
+    else:
+        mesh_trimesh = trimesh.load(mesh_filename)
+        subsamples = mesh_v
+    origins, ends, pts_bone_dist = pts2line(subsamples, bones)
+    pts_bone_visibility = calc_pts2bone_visible_mat(mesh_trimesh, origins, ends)
+    pts_bone_visibility = pts_bone_visibility.reshape(len(bones), len(subsamples)).transpose()
+    pts_bone_dist = pts_bone_dist.reshape(len(bones), len(subsamples)).transpose()
+    # remove visible points which are too far
+    for b in range(pts_bone_visibility.shape[1]):
+        visible_pts = np.argwhere(pts_bone_visibility[:, b] == 1).squeeze(1)
+        if len(visible_pts) == 0:
+            continue
+        threshold_b = np.percentile(pts_bone_dist[visible_pts, b], 15)
+        pts_bone_visibility[pts_bone_dist[:, b] > 1.3 * threshold_b, b] = False
+
+    visible_matrix = np.zeros(pts_bone_visibility.shape)
+    visible_matrix[np.where(pts_bone_visibility == 1)] = pts_bone_dist[np.where(pts_bone_visibility == 1)]
+    for c in range(visible_matrix.shape[1]):
+        unvisible_pts = np.argwhere(pts_bone_visibility[:, c] == 0).squeeze(1)
+        visible_pts = np.argwhere(pts_bone_visibility[:, c] == 1).squeeze(1)
+        if len(visible_pts) == 0:
+            visible_matrix[:, c] = pts_bone_dist[:, c]
+            continue
+        for r in unvisible_pts:
+            dist1 = np.min(surface_geodesic[r, visible_pts])
+            nn_visible = visible_pts[np.argmin(surface_geodesic[r, visible_pts])]
+            if np.isinf(dist1):
+                visible_matrix[r, c] = 8.0 + pts_bone_dist[r, c]
+            else:
+                visible_matrix[r, c] = dist1 + visible_matrix[nn_visible, c]
+    if subsampling:
+        nn_dist = np.sum((mesh_v[:, np.newaxis, :] - subsamples[np.newaxis, ...])**2, axis=2)
+        nn_ind = np.argmin(nn_dist, axis=1)
+        visible_matrix = visible_matrix[nn_ind, :]
+        os.remove(mesh_filename.replace(".obj", "_simplified.obj"))
+    return visible_matrix
+
+
+def predict_skinning(input_data, pred_skel, skin_pred_net, surface_geodesic, mesh_filename, subsampling=False):
+    """
+    predict skinning
+    :param input_data: wrapped input data
+    :param pred_skel: predicted skeleton
+    :param skin_pred_net: network to predict skinning weights
+    :param surface_geodesic: geodesic distance matrix of all vertices
+    :param mesh_filename: mesh filename
+    :return: predicted rig with skinning weights information
+    """
+    global device, output_folder
+    num_nearest_bone = 5
+    bones, bone_names, bone_isleaf = get_bones(pred_skel)
+    mesh_v = input_data.pos.data.cpu().numpy()
+    print("     calculating volumetric geodesic distance from vertices to bone. This step takes some time...")
+    geo_dist = calc_geodesic_matrix(bones, mesh_v, surface_geodesic, mesh_filename, subsampling=subsampling)
+    input_samples = []  # joint_pos (x, y, z), (bone_id, 1/D)*5
+    loss_mask = []
+    skin_nn = []
+    for v_id in range(len(mesh_v)):
+        geo_dist_v = geo_dist[v_id]
+        bone_id_near_to_far = np.argsort(geo_dist_v)
+        this_sample = []
+        this_nn = []
+        this_mask = []
+        for i in range(num_nearest_bone):
+            if i >= len(bones):
+                this_sample += bones[bone_id_near_to_far[0]].tolist()
+                this_sample.append(1.0 / (geo_dist_v[bone_id_near_to_far[0]] + 1e-10))
+                this_sample.append(bone_isleaf[bone_id_near_to_far[0]])
+                this_nn.append(0)
+                this_mask.append(0)
+            else:
+                skel_bone_id = bone_id_near_to_far[i]
+                this_sample += bones[skel_bone_id].tolist()
+                this_sample.append(1.0 / (geo_dist_v[skel_bone_id] + 1e-10))
+                this_sample.append(bone_isleaf[skel_bone_id])
+                this_nn.append(skel_bone_id)
+                this_mask.append(1)
+        input_samples.append(np.array(this_sample)[np.newaxis, :])
+        skin_nn.append(np.array(this_nn)[np.newaxis, :])
+        loss_mask.append(np.array(this_mask)[np.newaxis, :])
+
+    skin_input = np.concatenate(input_samples, axis=0)
+    loss_mask = np.concatenate(loss_mask, axis=0)
+    skin_nn = np.concatenate(skin_nn, axis=0)
+    skin_input = torch.from_numpy(skin_input).float()
+    input_data.skin_input = skin_input
+    input_data.to(device)
+
+    skin_pred = skin_pred_net(input_data)
+    skin_pred = torch.softmax(skin_pred, dim=1)
+    skin_pred = skin_pred.data.cpu().numpy()
+    skin_pred = skin_pred * loss_mask
+
+    skin_nn = skin_nn[:, 0:num_nearest_bone]
+    skin_pred_full = np.zeros((len(skin_pred), len(bone_names)))
+    for v in range(len(skin_pred)):
+        for nn_id in range(len(skin_nn[v, :])):
+            skin_pred_full[v, skin_nn[v, nn_id]] = skin_pred[v, nn_id]
+    print("     filtering skinning prediction")
+    tpl_e = input_data.tpl_edge_index.data.cpu().numpy()
+    skin_pred_full = post_filter(skin_pred_full, tpl_e, num_ring=1)
+    skin_pred_full[skin_pred_full < np.max(skin_pred_full, axis=1, keepdims=True) * 0.35] = 0.0
+    skin_pred_full = skin_pred_full / (skin_pred_full.sum(axis=1, keepdims=True) + 1e-10)
+    skel_res = assemble_skel_skin(pred_skel, skin_pred_full)
+    return skel_res
+
+
+def tranfer_to_ori_mesh(filename_ori, filename_remesh, pred_rig):
+    """
+    convert the predicted rig of remeshed model to the rig of the original model.
+    Just assign skinning weight based on nearest neighbor
+    :param filename_ori: original mesh filename
+    :param filename_remesh: remeshed mesh filename
+    :param pred_rig: predicted rig
+    :return: predicted rig for original mesh
+    """
+    mesh_remesh = o3d.io.read_triangle_mesh(filename_remesh)
+    mesh_ori = o3d.io.read_triangle_mesh(filename_ori)
+    tranfer_rig = Info()
+
+    vert_remesh = np.asarray(mesh_remesh.vertices)
+    vert_ori = np.asarray(mesh_ori.vertices)
+
+    vertice_distance = np.sqrt(np.sum((vert_ori[np.newaxis, ...] - vert_remesh[:, np.newaxis, :]) ** 2, axis=2))
+    vertice_raw_id = np.argmin(vertice_distance, axis=0)  # nearest vertex id on the fixed mesh for each vertex on the remeshed mesh
+
+    tranfer_rig.root = pred_rig.root
+    tranfer_rig.joint_pos = pred_rig.joint_pos
+    new_skin = []
+    for v in range(len(vert_ori)):
+        skin_v = [v]
+        v_nn = vertice_raw_id[v]
+        skin_v += pred_rig.joint_skin[v_nn][1:]
+        new_skin.append(skin_v)
+    tranfer_rig.joint_skin = new_skin
+    return tranfer_rig
+
+
+if __name__ == '__main__':
+    input_folder = "quick_start/"
+
+    # downsample_skinning is used to speed up the calculation of volumetric geodesic distance
+    # and to save cpu memory in skinning calculation.
+    # Change to False to be more accurate but less efficient.
+    downsample_skinning = True
+
+    # load all weights
+    print("loading all networks...")
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+    jointNet = JOINTNET()
+    jointNet.to(device)
+    jointNet.eval()
+    jointNet_checkpoint = torch.load('checkpoints/gcn_meanshift/model_best.pth.tar')
+    jointNet.load_state_dict(jointNet_checkpoint['state_dict'])
+    print("     joint prediction network loaded.")
+
+    rootNet = ROOTNET()
+    rootNet.to(device)
+    rootNet.eval()
+    rootNet_checkpoint = torch.load('checkpoints/rootnet/model_best.pth.tar')
+    rootNet.load_state_dict(rootNet_checkpoint['state_dict'])
+    print("     root prediction network loaded.")
+
+    boneNet = BONENET()
+    boneNet.to(device)
+    boneNet.eval()
+    boneNet_checkpoint = torch.load('checkpoints/bonenet/model_best.pth.tar')
+    boneNet.load_state_dict(boneNet_checkpoint['state_dict'])
+    print("     connection prediction network loaded.")
+
+    skinNet = SKINNET(nearest_bone=5, use_Dg=True, use_Lf=True)
+    skinNet_checkpoint = torch.load('checkpoints/skinnet/model_best.pth.tar')
+    skinNet.load_state_dict(skinNet_checkpoint['state_dict'])
+    skinNet.to(device)
+    skinNet.eval()
+    print("     skinning prediction network loaded.")
+
+    # Here we provide 16~17 examples. For best results, we will need to override the learned bandwidth and its associated threshold
+    # To process other input characters, please first try the learned bandwidth (0.0429 in the provided model), and the default threshold 1e-5.
+    # We also use these two default parameters for processing all test models in batch.
+
+    #model_id, bandwidth, threshold = "smith", None, 1e-5
+    model_id, bandwidth, threshold = "17872", 0.045, 0.75e-5
+    #model_id, bandwidth, threshold = "8210", 0.05, 1e-5
+    #model_id, bandwidth, threshold = "8330", 0.05, 0.8e-5
+    #model_id, bandwidth, threshold = "9477", 0.043, 2.5e-5
+    #model_id, bandwidth, threshold = "17364", 0.058, 0.3e-5
+    #model_id, bandwidth, threshold = "15930", 0.055, 0.4e-5
+    #model_id, bandwidth, threshold = "8333", 0.04, 2e-5
+    #model_id, bandwidth, threshold = "8338", 0.052, 0.9e-5
+    #model_id, bandwidth, threshold = "3318", 0.03, 0.92e-5
+    #model_id, bandwidth, threshold = "15446", 0.032, 0.58e-5
+    #model_id, bandwidth, threshold = "1347", 0.062, 3e-5
+    #model_id, bandwidth, threshold = "11814", 0.06, 0.6e-5
+    #model_id, bandwidth, threshold = "2982", 0.045, 0.3e-5
+    #model_id, bandwidth, threshold = "2586", 0.05, 0.6e-5
+    #model_id, bandwidth, threshold = "8184", 0.05, 0.4e-5
+    #model_id, bandwidth, threshold = "9000", 0.035, 0.16e-5
+
+    # create data used for inferece
+    print("creating data for model ID {:s}".format(model_id))
+    mesh_filename = os.path.join(input_folder, '{:s}_remesh.obj'.format(model_id))
+    if not os.path.exists(mesh_filename):
+        mesh_ori_filename = os.path.join(input_folder, '{:s}_ori.obj'.format(model_id))
+        mesh_ori = o3d.io.read_triangle_mesh(mesh_ori_filename)
+        if len(np.asarray(mesh_ori.vertices)) == 0:
+            print(f"Please name your input model as {model_id}_ori.obj")
+            exit()
+        mesh_remesh = mesh_ori.simplify_quadric_decimation(4000)  # adjust vertices between 1K - 5K
+        o3d.io.write_triangle_mesh(mesh_filename, mesh_remesh)
+
+    data, vox, surface_geodesic, translation_normalize, scale_normalize = create_single_data(mesh_filename)
+    data.to(device)
+
+    print("predicting joints")
+    data = predict_joints(data, vox, jointNet, threshold, bandwidth=bandwidth,
+                          mesh_filename=mesh_filename.replace("_remesh.obj", "_normalized.obj"))
+    data.to(device)
+    print("predicting connectivity")
+    pred_skeleton = predict_skeleton(data, vox, rootNet, boneNet,
+                                     mesh_filename=mesh_filename.replace("_remesh.obj", "_normalized.obj"))
+    print("predicting skinning")
+    pred_rig = predict_skinning(data, pred_skeleton, skinNet, surface_geodesic,
+                                mesh_filename.replace("_remesh.obj", "_normalized.obj"),
+                                subsampling=downsample_skinning)
+
+    # here we reverse the normalization to the original scale and position
+    pred_rig.normalize(scale_normalize, -translation_normalize)
+
+    print("Saving result")
+    if True:
+        # here we use original mesh tesselation (without remeshing)
+        mesh_filename_ori = os.path.join(input_folder, '{:s}_ori.obj'.format(model_id))
+        pred_rig = tranfer_to_ori_mesh(mesh_filename_ori, mesh_filename, pred_rig)
+        pred_rig.save(mesh_filename_ori.replace('.obj', '_rig.txt'))
+    else:
+        # here we use remeshed mesh
+        pred_rig.save(mesh_filename.replace('.obj', '_rig.txt'))
+    print("Done!")

RigNet/quick_start/11814_ori.fbx

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RigNet/quick_start/11814_ori_rig.txt

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+joints root 0.00000000 0.26184708 0.14769544
+joints root_dup_0 0.00555594 0.26740304 0.15325138
+joints root_dup_1 0.00532755 0.26717463 0.15302299
+joints root_dup_2 0.00436341 0.26621050 0.15205885
+joints root_dup_3 0.00555594 0.26740304 0.15325138
+joints joint_0 -0.09531559 0.10361934 0.13438132
+joints joint_2 0.00000000 0.26601532 -0.02984070
+joints joint_7 0.00000000 0.25475711 0.29296958
+joints joint_8 0.09531559 0.10361934 0.13438132
+joints joint_2_dup_0 0.00315637 0.26917168 -0.02668433
+joints joint_2_dup_1 0.00514538 0.27116069 -0.02469531
+joints joint_4 0.00000000 0.36090925 0.01559903
+joints joint_5 0.00000000 0.25808817 -0.20117018
+joints joint_3 0.00000000 0.23391138 -0.34799549
+joints joint_6 0.00000000 0.17535001 -0.41275612
+root root
+skin 0 root_dup_2 0.4212 root_dup_3 0.5788 
+skin 1 root_dup_2 0.5799 root_dup_3 0.4201 
+skin 2 root_dup_2 1.0000 
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RigNet/quick_start/1347_ori.fbx

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+oid sha256:d36c2633f14b88892ac03653d0c15ff43191700865b01847233597d0da68f940
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RigNet/quick_start/1347_ori_rig.txt

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RigNet/quick_start/15446_ori_rig.txt

@@ -0,0 +1,836 @@
+joints root 0.00000000 0.22579750 -0.22022524
+joints root_dup_0 0.00543076 0.23122826 -0.21479449
+joints root_dup_1 0.00291324 0.22871074 -0.21731201
+joints root_dup_2 0.00163954 0.22743705 -0.21858570
+joints root_dup_3 0.00543076 0.23122826 -0.21479449
+joints joint_1 -0.11709178 0.09379947 -0.26067549
+joints joint_9 0.00000000 0.22302763 -0.31729373
+joints joint_10 0.00000000 0.22077297 -0.16580530
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RigNet/quick_start/15930_ori_rig.txt

@@ -0,0 +1,204 @@
+joints root 0.00000000 0.26804522 0.00153997
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+skin 159 joint_7 1.0000 
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+hier root root_dup_0
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+hier joint_2 joint_1
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+hier joint_1 joint_0
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RigNet/quick_start/17364_ori.fbx

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