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MSPC for I2I

This repository is by Yanwu Xu and contains the PyTorch source code to reproduce the experiments in our CVPR2022 paper Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation by Yanwu Xu, Shaoan Xie, Wenhao Wu, Kun Zhang, Mingming Gong* and Kayhan Batmanghelich* (* Equal Contribution)

Purturbation ConsistencySpatial Alignment
0.50.5

Face Pose Transfer data can be downloaded here

Experiments on real data

To run the code on the face pose transfer data. 1. download the data from above link 2. unzip the data to the ./data folder 3. sh run_gcpert.sh

Qualitative Results

Comparison
1.0

Dynamic of Spatial Transformer T

Comparison
1.0

Citation

@InProceedings{Xu_2022_CVPR,
author = {Xu, Yanwu and Xie, Shaoan and Wu, Wenhao and Zhang, Kun and Gong, Mingming and Batmanghelich, Kayhan},
title = {Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {18311-18320}
}

Acknowledgments

This work was partially supported by NIH Award Number 1R01HL141813-01, NSF 1839332 Tripod+X, SAP SE, and Pennsylvania Department of Health. We are grateful for the computational resources provided by Pittsburgh SuperComputing grant number TG-ASC170024. MG is supported by Australian Research Council Project DE210101624. KZ would like to acknowledge the support by the National Institutes of Health (NIH) under Contract R01HL159805, by the NSF-Convergence Accelerator Track-D award #2134901, and by the United States Air Force under Contract No. C7715.

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Maximum Spatial Perturbation for Image-to-Image Translation (Official Implementation)

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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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MSPC for I2I
This repository is by Yanwu Xu
and contains the PyTorch source code to
reproduce the experiments in our CVPR2022 paper Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation by Yanwu Xu, Shaoan Xie, Wenhao Wu, Kun Zhang, Mingming Gong* and Kayhan Batmanghelich* (* Equal Contribution)
Purturbation ConsistencySpatial Alignment
0.50.5
Face Pose Transfer data can be downloaded here
Experiments on real data
To run the code on the face pose transfer data. 1. download the data from above link 2. unzip the data to the ./data folder 3. sh run_gcpert.sh
Qualitative Results
Comparison
1.0
Dynamic of Spatial Transformer T
Comparison
1.0
Citation
@InProceedings{Xu_2022_CVPR,
author = {Xu, Yanwu and Xie, Shaoan and Wu, Wenhao and Zhang, Kun and Gong, Mingming and Batmanghelich, Kayhan},
title = {Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {18311-18320}
}

Acknowledgments
This work was partially supported by NIH Award Number 1R01HL141813-01, NSF 1839332 Tripod+X, SAP SE, and Pennsylvania Department of Health. We are grateful for the computational resources provided by Pittsburgh SuperComputing grant number TG-ASC170024. MG is supported by Australian Research Council Project DE210101624. KZ would like to acknowledge the support by the National Institutes of Health (NIH) under Contract R01HL159805, by the NSF-Convergence Accelerator Track-D award #2134901, and by the United States Air Force under Contract No. C7715.
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Maximum Spatial Perturbation for Image-to-Image Translation (Official Implementation)
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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('^' + ".*" + '

Skip to content
BranchesTags
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MSPC for I2I
This repository is by Yanwu Xu
and contains the PyTorch source code to
reproduce the experiments in our CVPR2022 paper Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation by Yanwu Xu, Shaoan Xie, Wenhao Wu, Kun Zhang, Mingming Gong* and Kayhan Batmanghelich* (* Equal Contribution)
Purturbation ConsistencySpatial Alignment
0.50.5
Face Pose Transfer data can be downloaded here
Experiments on real data
To run the code on the face pose transfer data. 1. download the data from above link 2. unzip the data to the ./data folder 3. sh run_gcpert.sh
Qualitative Results
Comparison
1.0
Dynamic of Spatial Transformer T
Comparison
1.0
Citation
@InProceedings{Xu_2022_CVPR,
author = {Xu, Yanwu and Xie, Shaoan and Wu, Wenhao and Zhang, Kun and Gong, Mingming and Batmanghelich, Kayhan},
title = {Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {18311-18320}
}

Acknowledgments
This work was partially supported by NIH Award Number 1R01HL141813-01, NSF 1839332 Tripod+X, SAP SE, and Pennsylvania Department of Health. We are grateful for the computational resources provided by Pittsburgh SuperComputing grant number TG-ASC170024. MG is supported by Australian Research Council Project DE210101624. KZ would like to acknowledge the support by the National Institutes of Health (NIH) under Contract R01HL159805, by the NSF-Convergence Accelerator Track-D award #2134901, and by the United States Air Force under Contract No. C7715.
About
Maximum Spatial Perturbation for Image-to-Image Translation (Official Implementation)
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63 stars
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2 watching
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2 forks
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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('^' + ".*" + '

Skip to content
BranchesTags
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MSPC for I2I
This repository is by Yanwu Xu
and contains the PyTorch source code to
reproduce the experiments in our CVPR2022 paper Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation by Yanwu Xu, Shaoan Xie, Wenhao Wu, Kun Zhang, Mingming Gong* and Kayhan Batmanghelich* (* Equal Contribution)
Purturbation ConsistencySpatial Alignment
0.50.5
Face Pose Transfer data can be downloaded here
Experiments on real data
To run the code on the face pose transfer data. 1. download the data from above link 2. unzip the data to the ./data folder 3. sh run_gcpert.sh
Qualitative Results
Comparison
1.0
Dynamic of Spatial Transformer T
Comparison
1.0
Citation
@InProceedings{Xu_2022_CVPR,
author = {Xu, Yanwu and Xie, Shaoan and Wu, Wenhao and Zhang, Kun and Gong, Mingming and Batmanghelich, Kayhan},
title = {Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {18311-18320}
}

Acknowledgments
This work was partially supported by NIH Award Number 1R01HL141813-01, NSF 1839332 Tripod+X, SAP SE, and Pennsylvania Department of Health. We are grateful for the computational resources provided by Pittsburgh SuperComputing grant number TG-ASC170024. MG is supported by Australian Research Council Project DE210101624. KZ would like to acknowledge the support by the National Institutes of Health (NIH) under Contract R01HL159805, by the NSF-Convergence Accelerator Track-D award #2134901, and by the United States Air Force under Contract No. C7715.
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Maximum Spatial Perturbation for Image-to-Image Translation (Official Implementation)
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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");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
MSPC for I2I
This repository is by Yanwu Xu
and contains the PyTorch source code to
reproduce the experiments in our CVPR2022 paper Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation by Yanwu Xu, Shaoan Xie, Wenhao Wu, Kun Zhang, Mingming Gong* and Kayhan Batmanghelich* (* Equal Contribution)
Purturbation ConsistencySpatial Alignment
0.50.5
Face Pose Transfer data can be downloaded here
Experiments on real data
To run the code on the face pose transfer data. 1. download the data from above link 2. unzip the data to the ./data folder 3. sh run_gcpert.sh
Qualitative Results
Comparison
1.0
Dynamic of Spatial Transformer T
Comparison
1.0
Citation
@InProceedings{Xu_2022_CVPR,
author = {Xu, Yanwu and Xie, Shaoan and Wu, Wenhao and Zhang, Kun and Gong, Mingming and Batmanghelich, Kayhan},
title = {Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {18311-18320}
}

Acknowledgments
This work was partially supported by NIH Award Number 1R01HL141813-01, NSF 1839332 Tripod+X, SAP SE, and Pennsylvania Department of Health. We are grateful for the computational resources provided by Pittsburgh SuperComputing grant number TG-ASC170024. MG is supported by Australian Research Council Project DE210101624. KZ would like to acknowledge the support by the National Institutes of Health (NIH) under Contract R01HL159805, by the NSF-Convergence Accelerator Track-D award #2134901, and by the United States Air Force under Contract No. C7715.
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Maximum Spatial Perturbation for Image-to-Image Translation (Official Implementation)
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63 stars
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2 watching
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2 forks
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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");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
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MSPC for I2I
This repository is by Yanwu Xu
and contains the PyTorch source code to
reproduce the experiments in our CVPR2022 paper Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation by Yanwu Xu, Shaoan Xie, Wenhao Wu, Kun Zhang, Mingming Gong* and Kayhan Batmanghelich* (* Equal Contribution)
Purturbation ConsistencySpatial Alignment
0.50.5
Face Pose Transfer data can be downloaded here
Experiments on real data
To run the code on the face pose transfer data. 1. download the data from above link 2. unzip the data to the ./data folder 3. sh run_gcpert.sh
Qualitative Results
Comparison
1.0
Dynamic of Spatial Transformer T
Comparison
1.0
Citation
@InProceedings{Xu_2022_CVPR,
author = {Xu, Yanwu and Xie, Shaoan and Wu, Wenhao and Zhang, Kun and Gong, Mingming and Batmanghelich, Kayhan},
title = {Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {18311-18320}
}

Acknowledgments
This work was partially supported by NIH Award Number 1R01HL141813-01, NSF 1839332 Tripod+X, SAP SE, and Pennsylvania Department of Health. We are grateful for the computational resources provided by Pittsburgh SuperComputing grant number TG-ASC170024. MG is supported by Australian Research Council Project DE210101624. KZ would like to acknowledge the support by the National Institutes of Health (NIH) under Contract R01HL159805, by the NSF-Convergence Accelerator Track-D award #2134901, and by the United States Air Force under Contract No. C7715.
About
Maximum Spatial Perturbation for Image-to-Image Translation (Official Implementation)
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63 stars
Watchers
2 watching
Forks
2 forks
Report repository
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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('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
MSPC for I2I
This repository is by Yanwu Xu
and contains the PyTorch source code to
reproduce the experiments in our CVPR2022 paper Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation by Yanwu Xu, Shaoan Xie, Wenhao Wu, Kun Zhang, Mingming Gong* and Kayhan Batmanghelich* (* Equal Contribution)
Purturbation ConsistencySpatial Alignment
0.50.5
Face Pose Transfer data can be downloaded here
Experiments on real data
To run the code on the face pose transfer data. 1. download the data from above link 2. unzip the data to the ./data folder 3. sh run_gcpert.sh
Qualitative Results
Comparison
1.0
Dynamic of Spatial Transformer T
Comparison
1.0
Citation
@InProceedings{Xu_2022_CVPR,
author = {Xu, Yanwu and Xie, Shaoan and Wu, Wenhao and Zhang, Kun and Gong, Mingming and Batmanghelich, Kayhan},
title = {Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {18311-18320}
}

Acknowledgments
This work was partially supported by NIH Award Number 1R01HL141813-01, NSF 1839332 Tripod+X, SAP SE, and Pennsylvania Department of Health. We are grateful for the computational resources provided by Pittsburgh SuperComputing grant number TG-ASC170024. MG is supported by Australian Research Council Project DE210101624. KZ would like to acknowledge the support by the National Institutes of Health (NIH) under Contract R01HL159805, by the NSF-Convergence Accelerator Track-D award #2134901, and by the United States Air Force under Contract No. C7715.
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Maximum Spatial Perturbation for Image-to-Image Translation (Official Implementation)
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, '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); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
MSPC for I2I
This repository is by Yanwu Xu
and contains the PyTorch source code to
reproduce the experiments in our CVPR2022 paper Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation by Yanwu Xu, Shaoan Xie, Wenhao Wu, Kun Zhang, Mingming Gong* and Kayhan Batmanghelich* (* Equal Contribution)
Purturbation ConsistencySpatial Alignment
0.50.5
Face Pose Transfer data can be downloaded here
Experiments on real data
To run the code on the face pose transfer data. 1. download the data from above link 2. unzip the data to the ./data folder 3. sh run_gcpert.sh
Qualitative Results
Comparison
1.0
Dynamic of Spatial Transformer T
Comparison
1.0
Citation
@InProceedings{Xu_2022_CVPR,
author = {Xu, Yanwu and Xie, Shaoan and Wu, Wenhao and Zhang, Kun and Gong, Mingming and Batmanghelich, Kayhan},
title = {Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {18311-18320}
}

Acknowledgments
This work was partially supported by NIH Award Number 1R01HL141813-01, NSF 1839332 Tripod+X, SAP SE, and Pennsylvania Department of Health. We are grateful for the computational resources provided by Pittsburgh SuperComputing grant number TG-ASC170024. MG is supported by Australian Research Council Project DE210101624. KZ would like to acknowledge the support by the National Institutes of Health (NIH) under Contract R01HL159805, by the NSF-Convergence Accelerator Track-D award #2134901, and by the United States Air Force under Contract No. C7715.
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Maximum Spatial Perturbation for Image-to-Image Translation (Official Implementation)
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