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DSFNet

Paper link: https://arxiv.org/abs/2305.11522
Project link: https://lhyfst.github.io/dsfnet/

Requirements

python 3.6.13
pytorch 1.7.1
cudatoolkit 10.1.243
imageio 2.15.0
numpy 1.19.2
opencv-python 4.7.0.72
PyYAML 6.0
scikit-image 0.17.2
torchvision 0.8.2
tqdm 4.64.1
trimesh 3.22.1

You can easily prepare the conda environment by conda create --name DSFNet --file requirements.txt

Prepare

Evaluation

  • Download AFLW2000-3D at http://www.cbsr.ia.ac.cn/users/xiangyuzhu/projects/3ddfa/main.htm .

  • Follow SADRNet to crop images and prepare the image directory. Or you can download the cropped images at link. Put them at data/dataset/AFLW2000_crop.

  • Run src/run/predict.py. In the returned text, nme3d, rec, MAE are the results of dense 3D dense face alignment, reconstruction, and head pose estimation.

Acknowledgements

We especially thank the contributors of the SADRNet codebase for providing helpful code.

About

Code for DSFNet: Dual Space Fusion Network for Occlusion-Robust Dense 3D Face Alignment

Resources

Readme
Activity

Stars

76 stars

Watchers

7 watching

Forks

4 forks
Report repository

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Used by

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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DSFNet
Paper link: https://arxiv.org/abs/2305.11522

Project link: https://lhyfst.github.io/dsfnet/
Requirements
python 3.6.13
pytorch 1.7.1
cudatoolkit 10.1.243
imageio 2.15.0
numpy 1.19.2
opencv-python 4.7.0.72
PyYAML 6.0
scikit-image 0.17.2
torchvision 0.8.2
tqdm 4.64.1
trimesh 3.22.1

You can easily prepare the conda environment by conda create --name DSFNet --file requirements.txt
Prepare
Evaluation
  • Download AFLW2000-3D at http://www.cbsr.ia.ac.cn/users/xiangyuzhu/projects/3ddfa/main.htm .
  • Follow SADRNet to crop images and prepare the image directory. Or you can download the cropped images at link. Put them at data/dataset/AFLW2000_crop.
  • Run src/run/predict.py. In the returned text, nme3d, rec, MAE are the results of dense 3D dense face alignment, reconstruction, and head pose estimation.
Acknowledgements
We especially thank the contributors of the SADRNet codebase for providing helpful code.
About
Code for DSFNet: Dual Space Fusion Network for Occlusion-Robust Dense 3D Face Alignment
Resources
Readme
Activity
Stars
76 stars
Watchers
7 watching
Forks
4 forks
Report repository
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Used by
Contributors
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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");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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config.py
requirements.txt
requirements.txt
Repository files navigation
DSFNet
Paper link: https://arxiv.org/abs/2305.11522

Project link: https://lhyfst.github.io/dsfnet/
Requirements
python 3.6.13
pytorch 1.7.1
cudatoolkit 10.1.243
imageio 2.15.0
numpy 1.19.2
opencv-python 4.7.0.72
PyYAML 6.0
scikit-image 0.17.2
torchvision 0.8.2
tqdm 4.64.1
trimesh 3.22.1

You can easily prepare the conda environment by conda create --name DSFNet --file requirements.txt
Prepare
Evaluation
  • Download AFLW2000-3D at http://www.cbsr.ia.ac.cn/users/xiangyuzhu/projects/3ddfa/main.htm .
  • Follow SADRNet to crop images and prepare the image directory. Or you can download the cropped images at link. Put them at data/dataset/AFLW2000_crop.
  • Run src/run/predict.py. In the returned text, nme3d, rec, MAE are the results of dense 3D dense face alignment, reconstruction, and head pose estimation.
Acknowledgements
We especially thank the contributors of the SADRNet codebase for providing helpful code.
About
Code for DSFNet: Dual Space Fusion Network for Occlusion-Robust Dense 3D Face Alignment
Resources
Readme
Activity
Stars
76 stars
Watchers
7 watching
Forks
4 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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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requirements.txt
Repository files navigation
DSFNet
Paper link: https://arxiv.org/abs/2305.11522

Project link: https://lhyfst.github.io/dsfnet/
Requirements
python 3.6.13
pytorch 1.7.1
cudatoolkit 10.1.243
imageio 2.15.0
numpy 1.19.2
opencv-python 4.7.0.72
PyYAML 6.0
scikit-image 0.17.2
torchvision 0.8.2
tqdm 4.64.1
trimesh 3.22.1

You can easily prepare the conda environment by conda create --name DSFNet --file requirements.txt
Prepare
Evaluation
  • Download AFLW2000-3D at http://www.cbsr.ia.ac.cn/users/xiangyuzhu/projects/3ddfa/main.htm .
  • Follow SADRNet to crop images and prepare the image directory. Or you can download the cropped images at link. Put them at data/dataset/AFLW2000_crop.
  • Run src/run/predict.py. In the returned text, nme3d, rec, MAE are the results of dense 3D dense face alignment, reconstruction, and head pose estimation.
Acknowledgements
We especially thank the contributors of the SADRNet codebase for providing helpful code.
About
Code for DSFNet: Dual Space Fusion Network for Occlusion-Robust Dense 3D Face Alignment
Resources
Readme
Activity
Stars
76 stars
Watchers
7 watching
Forks
4 forks
Report repository
Releases
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Used by
Contributors
Languages

, '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");
}
} 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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config.py
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requirements.txt
Repository files navigation
DSFNet
Paper link: https://arxiv.org/abs/2305.11522

Project link: https://lhyfst.github.io/dsfnet/
Requirements
python 3.6.13
pytorch 1.7.1
cudatoolkit 10.1.243
imageio 2.15.0
numpy 1.19.2
opencv-python 4.7.0.72
PyYAML 6.0
scikit-image 0.17.2
torchvision 0.8.2
tqdm 4.64.1
trimesh 3.22.1

You can easily prepare the conda environment by conda create --name DSFNet --file requirements.txt
Prepare
Evaluation
  • Download AFLW2000-3D at http://www.cbsr.ia.ac.cn/users/xiangyuzhu/projects/3ddfa/main.htm .
  • Follow SADRNet to crop images and prepare the image directory. Or you can download the cropped images at link. Put them at data/dataset/AFLW2000_crop.
  • Run src/run/predict.py. In the returned text, nme3d, rec, MAE are the results of dense 3D dense face alignment, reconstruction, and head pose estimation.
Acknowledgements
We especially thank the contributors of the SADRNet codebase for providing helpful code.
About
Code for DSFNet: Dual Space Fusion Network for Occlusion-Robust Dense 3D Face Alignment
Resources
Readme
Activity
Stars
76 stars
Watchers
7 watching
Forks
4 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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config.py
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requirements.txt
Repository files navigation
DSFNet
Paper link: https://arxiv.org/abs/2305.11522

Project link: https://lhyfst.github.io/dsfnet/
Requirements
python 3.6.13
pytorch 1.7.1
cudatoolkit 10.1.243
imageio 2.15.0
numpy 1.19.2
opencv-python 4.7.0.72
PyYAML 6.0
scikit-image 0.17.2
torchvision 0.8.2
tqdm 4.64.1
trimesh 3.22.1

You can easily prepare the conda environment by conda create --name DSFNet --file requirements.txt
Prepare
Evaluation
  • Download AFLW2000-3D at http://www.cbsr.ia.ac.cn/users/xiangyuzhu/projects/3ddfa/main.htm .
  • Follow SADRNet to crop images and prepare the image directory. Or you can download the cropped images at link. Put them at data/dataset/AFLW2000_crop.
  • Run src/run/predict.py. In the returned text, nme3d, rec, MAE are the results of dense 3D dense face alignment, reconstruction, and head pose estimation.
Acknowledgements
We especially thank the contributors of the SADRNet codebase for providing helpful code.
About
Code for DSFNet: Dual Space Fusion Network for Occlusion-Robust Dense 3D Face Alignment
Resources
Readme
Activity
Stars
76 stars
Watchers
7 watching
Forks
4 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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config.py
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requirements.txt
Repository files navigation
DSFNet
Paper link: https://arxiv.org/abs/2305.11522

Project link: https://lhyfst.github.io/dsfnet/
Requirements
python 3.6.13
pytorch 1.7.1
cudatoolkit 10.1.243
imageio 2.15.0
numpy 1.19.2
opencv-python 4.7.0.72
PyYAML 6.0
scikit-image 0.17.2
torchvision 0.8.2
tqdm 4.64.1
trimesh 3.22.1

You can easily prepare the conda environment by conda create --name DSFNet --file requirements.txt
Prepare
Evaluation
  • Download AFLW2000-3D at http://www.cbsr.ia.ac.cn/users/xiangyuzhu/projects/3ddfa/main.htm .
  • Follow SADRNet to crop images and prepare the image directory. Or you can download the cropped images at link. Put them at data/dataset/AFLW2000_crop.
  • Run src/run/predict.py. In the returned text, nme3d, rec, MAE are the results of dense 3D dense face alignment, reconstruction, and head pose estimation.
Acknowledgements
We especially thank the contributors of the SADRNet codebase for providing helpful code.
About
Code for DSFNet: Dual Space Fusion Network for Occlusion-Robust Dense 3D Face Alignment
Resources
Readme
Activity
Stars
76 stars
Watchers
7 watching
Forks
4 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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data/uv_data
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config.py
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requirements.txt
Repository files navigation
DSFNet
Paper link: https://arxiv.org/abs/2305.11522

Project link: https://lhyfst.github.io/dsfnet/
Requirements
python 3.6.13
pytorch 1.7.1
cudatoolkit 10.1.243
imageio 2.15.0
numpy 1.19.2
opencv-python 4.7.0.72
PyYAML 6.0
scikit-image 0.17.2
torchvision 0.8.2
tqdm 4.64.1
trimesh 3.22.1

You can easily prepare the conda environment by conda create --name DSFNet --file requirements.txt
Prepare
Evaluation
  • Download AFLW2000-3D at http://www.cbsr.ia.ac.cn/users/xiangyuzhu/projects/3ddfa/main.htm .
  • Follow SADRNet to crop images and prepare the image directory. Or you can download the cropped images at link. Put them at data/dataset/AFLW2000_crop.
  • Run src/run/predict.py. In the returned text, nme3d, rec, MAE are the results of dense 3D dense face alignment, reconstruction, and head pose estimation.
Acknowledgements
We especially thank the contributors of the SADRNet codebase for providing helpful code.
About
Code for DSFNet: Dual Space Fusion Network for Occlusion-Robust Dense 3D Face Alignment
Resources
Readme
Activity
Stars
76 stars
Watchers
7 watching
Forks
4 forks
Report repository
Releases
Packages
Used by
Contributors
Languages