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CoMA: Convolutional Mesh Autoencoders

Generating 3D Faces using Convolutional Mesh Autoencoders

This is an official repository of Generating 3D Faces using Convolutional Mesh Autoencoders

[Project Page][Arxiv]

UPDATE : Thank you for using and supporting this repository over the last two years. This will no longer be maintained. Alternatively, please use:

Requirements

This code is tested on Tensorflow 1.3. Requirements (including tensorflow) can be installed using:

pip install -r requirements.txt

Install mesh processing libraries from MPI-IS/mesh.

Data

Download the data from the Project Page.

Preprocess the data

python processData.py --data <PATH_OF_RAW_DATA> --save_path <PATH_TO_SAVE_PROCESSED DATA>

Data pre-processing creates numpy files for the interpolation experiment and extrapolation experiment (Section X of the paper). This creates 13 different train and test files. sliced_[train|test] is for the interpolation experiment. <EXPRESSION>_[train|test] are for cross validation cross 12 different expression sequences.

Training

To train, specify a name, and choose a particular train test split. For example,

python main.py --data data/sliced --name sliced

Testing

To test, specify a name, and data. For example,

python main.py --data data/sliced --name sliced --mode test

Reproducing results in the paper

Run the following script. The models are slightly better (~1% on average) than ones reported in the paper.

sh generateErrors.sh

Sampling

To sample faces from the latent space, specify a model and data. For example,

python main.py --data data/sliced --name sliced --mode latent

A face template pops up. You can then use the keys qwertyui to sample faces by moving forward in each of the 8 latent dimensions. Use asdfghjk to move backward in the latent space.

For more flexible usage, refer to lib/visualize_latent_space.py.

Acknowledgements

We thank Raffi Enficiaud and Ahmed Osman for pushing the release of psbody.mesh, an essential dependency for this project.

License

The code contained in this repository is under MIT License and is free for commercial and non-commercial purposes. The dependencies, in particular, MPI-IS/mesh and our data have their own license terms which can be found on their respective webpages. The dependencies and data are NOT covered by MIT License associated with this repository.

Related projects

CAPE (CVPR 2020): Based on CoMA, we build a conditional Mesh-VAE-GAN to learn the clothing deformation from the SMPL body model, making a generative, animatable model of people in clothing. A large-scale mesh dataset of clothed humans in motion is also included!

When using this code, please cite

Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, and Michael J. Black. "Generating 3D faces using Convolutional Mesh Autoencoders." European Conference on Computer Vision (ECCV) 2018.

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Convolutional Mesh Autoencoders for Generating 3D Faces

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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CoMA: Convolutional Mesh Autoencoders
Generating 3D Faces using Convolutional Mesh Autoencoders
This is an official repository of Generating 3D Faces using Convolutional Mesh Autoencoders
[Project Page][Arxiv]
UPDATE : Thank you for using and supporting this repository over the last two years. This will no longer be maintained. Alternatively, please use:
Requirements
This code is tested on Tensorflow 1.3. Requirements (including tensorflow) can be installed using:
pip install -r requirements.txt
Install mesh processing libraries from MPI-IS/mesh.
Data
Download the data from the Project Page.
Preprocess the data
python processData.py --data <PATH_OF_RAW_DATA> --save_path <PATH_TO_SAVE_PROCESSED DATA>
Data pre-processing creates numpy files for the interpolation experiment and extrapolation experiment (Section X of the paper).
This creates 13 different train and test files.
sliced_[train|test] is for the interpolation experiment.
<EXPRESSION>_[train|test] are for cross validation cross 12 different expression sequences.
Training
To train, specify a name, and choose a particular train test split. For example,
python main.py --data data/sliced --name sliced
Testing
To test, specify a name, and data. For example,
python main.py --data data/sliced --name sliced --mode test
Reproducing results in the paper
Run the following script. The models are slightly better (~1% on average) than ones reported in the paper.
sh generateErrors.sh
Sampling
To sample faces from the latent space, specify a model and data. For example,
python main.py --data data/sliced --name sliced --mode latent
A face template pops up. You can then use the keys qwertyui to sample faces by moving forward in each of the 8 latent dimensions. Use asdfghjk to move backward in the latent space.
For more flexible usage, refer to lib/visualize_latent_space.py.
Acknowledgements
We thank Raffi Enficiaud and Ahmed Osman for pushing the release of psbody.mesh, an essential dependency for this project.
License
The code contained in this repository is under MIT License and is free for commercial and non-commercial purposes. The dependencies, in particular, MPI-IS/mesh and our data have their own license terms which can be found on their respective webpages. The dependencies and data are NOT covered by MIT License associated with this repository.
Related projects
CAPE (CVPR 2020): Based on CoMA, we build a conditional Mesh-VAE-GAN to learn the clothing deformation from the SMPL body model, making a generative, animatable model of people in clothing. A large-scale mesh dataset of clothed humans in motion is also included!
When using this code, please cite
Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, and Michael J. Black. "Generating 3D faces using Convolutional Mesh Autoencoders." European Conference on Computer Vision (ECCV) 2018.
About
Convolutional Mesh Autoencoders for Generating 3D Faces
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Readme
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1 watching
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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CoMA: Convolutional Mesh Autoencoders
Generating 3D Faces using Convolutional Mesh Autoencoders
This is an official repository of Generating 3D Faces using Convolutional Mesh Autoencoders
[Project Page][Arxiv]
UPDATE : Thank you for using and supporting this repository over the last two years. This will no longer be maintained. Alternatively, please use:
Requirements
This code is tested on Tensorflow 1.3. Requirements (including tensorflow) can be installed using:
pip install -r requirements.txt
Install mesh processing libraries from MPI-IS/mesh.
Data
Download the data from the Project Page.
Preprocess the data
python processData.py --data <PATH_OF_RAW_DATA> --save_path <PATH_TO_SAVE_PROCESSED DATA>
Data pre-processing creates numpy files for the interpolation experiment and extrapolation experiment (Section X of the paper).
This creates 13 different train and test files.
sliced_[train|test] is for the interpolation experiment.
<EXPRESSION>_[train|test] are for cross validation cross 12 different expression sequences.
Training
To train, specify a name, and choose a particular train test split. For example,
python main.py --data data/sliced --name sliced
Testing
To test, specify a name, and data. For example,
python main.py --data data/sliced --name sliced --mode test
Reproducing results in the paper
Run the following script. The models are slightly better (~1% on average) than ones reported in the paper.
sh generateErrors.sh
Sampling
To sample faces from the latent space, specify a model and data. For example,
python main.py --data data/sliced --name sliced --mode latent
A face template pops up. You can then use the keys qwertyui to sample faces by moving forward in each of the 8 latent dimensions. Use asdfghjk to move backward in the latent space.
For more flexible usage, refer to lib/visualize_latent_space.py.
Acknowledgements
We thank Raffi Enficiaud and Ahmed Osman for pushing the release of psbody.mesh, an essential dependency for this project.
License
The code contained in this repository is under MIT License and is free for commercial and non-commercial purposes. The dependencies, in particular, MPI-IS/mesh and our data have their own license terms which can be found on their respective webpages. The dependencies and data are NOT covered by MIT License associated with this repository.
Related projects
CAPE (CVPR 2020): Based on CoMA, we build a conditional Mesh-VAE-GAN to learn the clothing deformation from the SMPL body model, making a generative, animatable model of people in clothing. A large-scale mesh dataset of clothed humans in motion is also included!
When using this code, please cite
Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, and Michael J. Black. "Generating 3D faces using Convolutional Mesh Autoencoders." European Conference on Computer Vision (ECCV) 2018.
About
Convolutional Mesh Autoencoders for Generating 3D Faces
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Readme
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1 watching
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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CoMA: Convolutional Mesh Autoencoders
Generating 3D Faces using Convolutional Mesh Autoencoders
This is an official repository of Generating 3D Faces using Convolutional Mesh Autoencoders
[Project Page][Arxiv]
UPDATE : Thank you for using and supporting this repository over the last two years. This will no longer be maintained. Alternatively, please use:
Requirements
This code is tested on Tensorflow 1.3. Requirements (including tensorflow) can be installed using:
pip install -r requirements.txt
Install mesh processing libraries from MPI-IS/mesh.
Data
Download the data from the Project Page.
Preprocess the data
python processData.py --data <PATH_OF_RAW_DATA> --save_path <PATH_TO_SAVE_PROCESSED DATA>
Data pre-processing creates numpy files for the interpolation experiment and extrapolation experiment (Section X of the paper).
This creates 13 different train and test files.
sliced_[train|test] is for the interpolation experiment.
<EXPRESSION>_[train|test] are for cross validation cross 12 different expression sequences.
Training
To train, specify a name, and choose a particular train test split. For example,
python main.py --data data/sliced --name sliced
Testing
To test, specify a name, and data. For example,
python main.py --data data/sliced --name sliced --mode test
Reproducing results in the paper
Run the following script. The models are slightly better (~1% on average) than ones reported in the paper.
sh generateErrors.sh
Sampling
To sample faces from the latent space, specify a model and data. For example,
python main.py --data data/sliced --name sliced --mode latent
A face template pops up. You can then use the keys qwertyui to sample faces by moving forward in each of the 8 latent dimensions. Use asdfghjk to move backward in the latent space.
For more flexible usage, refer to lib/visualize_latent_space.py.
Acknowledgements
We thank Raffi Enficiaud and Ahmed Osman for pushing the release of psbody.mesh, an essential dependency for this project.
License
The code contained in this repository is under MIT License and is free for commercial and non-commercial purposes. The dependencies, in particular, MPI-IS/mesh and our data have their own license terms which can be found on their respective webpages. The dependencies and data are NOT covered by MIT License associated with this repository.
Related projects
CAPE (CVPR 2020): Based on CoMA, we build a conditional Mesh-VAE-GAN to learn the clothing deformation from the SMPL body model, making a generative, animatable model of people in clothing. A large-scale mesh dataset of clothed humans in motion is also included!
When using this code, please cite
Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, and Michael J. Black. "Generating 3D faces using Convolutional Mesh Autoencoders." European Conference on Computer Vision (ECCV) 2018.
About
Convolutional Mesh Autoencoders for Generating 3D Faces
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
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} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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CoMA: Convolutional Mesh Autoencoders
Generating 3D Faces using Convolutional Mesh Autoencoders
This is an official repository of Generating 3D Faces using Convolutional Mesh Autoencoders
[Project Page][Arxiv]
UPDATE : Thank you for using and supporting this repository over the last two years. This will no longer be maintained. Alternatively, please use:
Requirements
This code is tested on Tensorflow 1.3. Requirements (including tensorflow) can be installed using:
pip install -r requirements.txt
Install mesh processing libraries from MPI-IS/mesh.
Data
Download the data from the Project Page.
Preprocess the data
python processData.py --data <PATH_OF_RAW_DATA> --save_path <PATH_TO_SAVE_PROCESSED DATA>
Data pre-processing creates numpy files for the interpolation experiment and extrapolation experiment (Section X of the paper).
This creates 13 different train and test files.
sliced_[train|test] is for the interpolation experiment.
<EXPRESSION>_[train|test] are for cross validation cross 12 different expression sequences.
Training
To train, specify a name, and choose a particular train test split. For example,
python main.py --data data/sliced --name sliced
Testing
To test, specify a name, and data. For example,
python main.py --data data/sliced --name sliced --mode test
Reproducing results in the paper
Run the following script. The models are slightly better (~1% on average) than ones reported in the paper.
sh generateErrors.sh
Sampling
To sample faces from the latent space, specify a model and data. For example,
python main.py --data data/sliced --name sliced --mode latent
A face template pops up. You can then use the keys qwertyui to sample faces by moving forward in each of the 8 latent dimensions. Use asdfghjk to move backward in the latent space.
For more flexible usage, refer to lib/visualize_latent_space.py.
Acknowledgements
We thank Raffi Enficiaud and Ahmed Osman for pushing the release of psbody.mesh, an essential dependency for this project.
License
The code contained in this repository is under MIT License and is free for commercial and non-commercial purposes. The dependencies, in particular, MPI-IS/mesh and our data have their own license terms which can be found on their respective webpages. The dependencies and data are NOT covered by MIT License associated with this repository.
Related projects
CAPE (CVPR 2020): Based on CoMA, we build a conditional Mesh-VAE-GAN to learn the clothing deformation from the SMPL body model, making a generative, animatable model of people in clothing. A large-scale mesh dataset of clothed humans in motion is also included!
When using this code, please cite
Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, and Michael J. Black. "Generating 3D faces using Convolutional Mesh Autoencoders." European Conference on Computer Vision (ECCV) 2018.
About
Convolutional Mesh Autoencoders for Generating 3D Faces
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Readme
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
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} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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CoMA: Convolutional Mesh Autoencoders
Generating 3D Faces using Convolutional Mesh Autoencoders
This is an official repository of Generating 3D Faces using Convolutional Mesh Autoencoders
[Project Page][Arxiv]
UPDATE : Thank you for using and supporting this repository over the last two years. This will no longer be maintained. Alternatively, please use:
Requirements
This code is tested on Tensorflow 1.3. Requirements (including tensorflow) can be installed using:
pip install -r requirements.txt
Install mesh processing libraries from MPI-IS/mesh.
Data
Download the data from the Project Page.
Preprocess the data
python processData.py --data <PATH_OF_RAW_DATA> --save_path <PATH_TO_SAVE_PROCESSED DATA>
Data pre-processing creates numpy files for the interpolation experiment and extrapolation experiment (Section X of the paper).
This creates 13 different train and test files.
sliced_[train|test] is for the interpolation experiment.
<EXPRESSION>_[train|test] are for cross validation cross 12 different expression sequences.
Training
To train, specify a name, and choose a particular train test split. For example,
python main.py --data data/sliced --name sliced
Testing
To test, specify a name, and data. For example,
python main.py --data data/sliced --name sliced --mode test
Reproducing results in the paper
Run the following script. The models are slightly better (~1% on average) than ones reported in the paper.
sh generateErrors.sh
Sampling
To sample faces from the latent space, specify a model and data. For example,
python main.py --data data/sliced --name sliced --mode latent
A face template pops up. You can then use the keys qwertyui to sample faces by moving forward in each of the 8 latent dimensions. Use asdfghjk to move backward in the latent space.
For more flexible usage, refer to lib/visualize_latent_space.py.
Acknowledgements
We thank Raffi Enficiaud and Ahmed Osman for pushing the release of psbody.mesh, an essential dependency for this project.
License
The code contained in this repository is under MIT License and is free for commercial and non-commercial purposes. The dependencies, in particular, MPI-IS/mesh and our data have their own license terms which can be found on their respective webpages. The dependencies and data are NOT covered by MIT License associated with this repository.
Related projects
CAPE (CVPR 2020): Based on CoMA, we build a conditional Mesh-VAE-GAN to learn the clothing deformation from the SMPL body model, making a generative, animatable model of people in clothing. A large-scale mesh dataset of clothed humans in motion is also included!
When using this code, please cite
Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, and Michael J. Black. "Generating 3D faces using Convolutional Mesh Autoencoders." European Conference on Computer Vision (ECCV) 2018.
About
Convolutional Mesh Autoencoders for Generating 3D Faces
Resources
Readme
License
Activity
Stars
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1 watching
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0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
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} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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CoMA: Convolutional Mesh Autoencoders
Generating 3D Faces using Convolutional Mesh Autoencoders
This is an official repository of Generating 3D Faces using Convolutional Mesh Autoencoders
[Project Page][Arxiv]
UPDATE : Thank you for using and supporting this repository over the last two years. This will no longer be maintained. Alternatively, please use:
Requirements
This code is tested on Tensorflow 1.3. Requirements (including tensorflow) can be installed using:
pip install -r requirements.txt
Install mesh processing libraries from MPI-IS/mesh.
Data
Download the data from the Project Page.
Preprocess the data
python processData.py --data <PATH_OF_RAW_DATA> --save_path <PATH_TO_SAVE_PROCESSED DATA>
Data pre-processing creates numpy files for the interpolation experiment and extrapolation experiment (Section X of the paper).
This creates 13 different train and test files.
sliced_[train|test] is for the interpolation experiment.
<EXPRESSION>_[train|test] are for cross validation cross 12 different expression sequences.
Training
To train, specify a name, and choose a particular train test split. For example,
python main.py --data data/sliced --name sliced
Testing
To test, specify a name, and data. For example,
python main.py --data data/sliced --name sliced --mode test
Reproducing results in the paper
Run the following script. The models are slightly better (~1% on average) than ones reported in the paper.
sh generateErrors.sh
Sampling
To sample faces from the latent space, specify a model and data. For example,
python main.py --data data/sliced --name sliced --mode latent
A face template pops up. You can then use the keys qwertyui to sample faces by moving forward in each of the 8 latent dimensions. Use asdfghjk to move backward in the latent space.
For more flexible usage, refer to lib/visualize_latent_space.py.
Acknowledgements
We thank Raffi Enficiaud and Ahmed Osman for pushing the release of psbody.mesh, an essential dependency for this project.
License
The code contained in this repository is under MIT License and is free for commercial and non-commercial purposes. The dependencies, in particular, MPI-IS/mesh and our data have their own license terms which can be found on their respective webpages. The dependencies and data are NOT covered by MIT License associated with this repository.
Related projects
CAPE (CVPR 2020): Based on CoMA, we build a conditional Mesh-VAE-GAN to learn the clothing deformation from the SMPL body model, making a generative, animatable model of people in clothing. A large-scale mesh dataset of clothed humans in motion is also included!
When using this code, please cite
Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, and Michael J. Black. "Generating 3D faces using Convolutional Mesh Autoencoders." European Conference on Computer Vision (ECCV) 2018.
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CoMA: Convolutional Mesh Autoencoders
Generating 3D Faces using Convolutional Mesh Autoencoders
This is an official repository of Generating 3D Faces using Convolutional Mesh Autoencoders
[Project Page][Arxiv]
UPDATE : Thank you for using and supporting this repository over the last two years. This will no longer be maintained. Alternatively, please use:
Requirements
This code is tested on Tensorflow 1.3. Requirements (including tensorflow) can be installed using:
pip install -r requirements.txt
Install mesh processing libraries from MPI-IS/mesh.
Data
Download the data from the Project Page.
Preprocess the data
python processData.py --data <PATH_OF_RAW_DATA> --save_path <PATH_TO_SAVE_PROCESSED DATA>
Data pre-processing creates numpy files for the interpolation experiment and extrapolation experiment (Section X of the paper).
This creates 13 different train and test files.
sliced_[train|test] is for the interpolation experiment.
<EXPRESSION>_[train|test] are for cross validation cross 12 different expression sequences.
Training
To train, specify a name, and choose a particular train test split. For example,
python main.py --data data/sliced --name sliced
Testing
To test, specify a name, and data. For example,
python main.py --data data/sliced --name sliced --mode test
Reproducing results in the paper
Run the following script. The models are slightly better (~1% on average) than ones reported in the paper.
sh generateErrors.sh
Sampling
To sample faces from the latent space, specify a model and data. For example,
python main.py --data data/sliced --name sliced --mode latent
A face template pops up. You can then use the keys qwertyui to sample faces by moving forward in each of the 8 latent dimensions. Use asdfghjk to move backward in the latent space.
For more flexible usage, refer to lib/visualize_latent_space.py.
Acknowledgements
We thank Raffi Enficiaud and Ahmed Osman for pushing the release of psbody.mesh, an essential dependency for this project.
License
The code contained in this repository is under MIT License and is free for commercial and non-commercial purposes. The dependencies, in particular, MPI-IS/mesh and our data have their own license terms which can be found on their respective webpages. The dependencies and data are NOT covered by MIT License associated with this repository.
Related projects
CAPE (CVPR 2020): Based on CoMA, we build a conditional Mesh-VAE-GAN to learn the clothing deformation from the SMPL body model, making a generative, animatable model of people in clothing. A large-scale mesh dataset of clothed humans in motion is also included!
When using this code, please cite
Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, and Michael J. Black. "Generating 3D faces using Convolutional Mesh Autoencoders." European Conference on Computer Vision (ECCV) 2018.
About
Convolutional Mesh Autoencoders for Generating 3D Faces
Resources
Readme
License
Activity
Stars
2 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages