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기존 text to 3D 인공지능 모델은 input prompt를 고정하여 사용하였다. 본 프로젝트에서는 chatbot을 사용하여 input prompt를 보안하고 input의 부족한 부분을 찾아낸다.

chatbot은 Large Language Model(LLM)인 Flan-T5를 사용하여 구현하였다. 또한 input의 부족한 태그를 찾기 위하여 distillBert를 사용해 NER모델을 구현하였다.

text-to-2D에선 stable diffusion을 사용하였고, 2D-to-3D에선 shape-e를 사용하였다.

Table of Contents

  1. Samples
  2. Recommended-specifications
  3. Usage

Samples

Recommended-specifications

  • Ubuntu 18.04 with python 3.9 & torch 2.0.1 + CUDA 11.7 on a RTX 2080Ti and NVIDIA T4

  • GPU
    at least 16G of vram(GPU Memory)

  • Python 3
    We tested all process(text processing, image generation, 3d reconstruction) on Python 3.9. So we do not guarantee other python version but it may also be working on other python version.

Usage

Acknowledgement

  • stable-dreamfusion
@misc{stable-dreamfusion,
Author = {Jiaxiang Tang},
Year = {2022},
Note = {https://github.com/ashawkey/stable-dreamfusion},
Title = {Stable-dreamfusion: Text-to-3D with Stable-diffusion}
}
  • DreamFusion Paper
@article{poole2022dreamfusion,
author = {Poole, Ben and Jain, Ajay and Barron, Jonathan T. and Mildenhall, Ben},
title = {DreamFusion: Text-to-3D using 2D Diffusion},
journal = {arXiv},
year = {2022},
}
  • Realfusion
@inproceedings{melaskyriazi2023realfusion,
author = {Melas-Kyriazi, Luke and Rupprecht, Christian and Laina, Iro and Vedaldi, Andrea},
title = {RealFusion: 360 Reconstruction of Any Object from a Single Image},
booktitle={CVPR}
year = {2023},
url = {https://arxiv.org/abs/2302.10663},
}

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

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Free3D
기존 text to 3D 인공지능 모델은 input prompt를 고정하여 사용하였다.
본 프로젝트에서는 chatbot을 사용하여 input prompt를 보안하고 input의 부족한 부분을 찾아낸다.
chatbot은 Large Language Model(LLM)인 Flan-T5를 사용하여 구현하였다.
또한 input의 부족한 태그를 찾기 위하여 distillBert를 사용해 NER모델을 구현하였다.
text-to-2D에선 stable diffusion을 사용하였고, 2D-to-3D에선 shape-e를 사용하였다.
Table of Contents
  1. Samples
  2. Recommended-specifications
  3. Usage
Samples
Recommended-specifications
  • Ubuntu 18.04 with python 3.9 & torch 2.0.1 + CUDA 11.7 on a RTX 2080Ti and NVIDIA T4
  • GPU
    
at least 16G of vram(GPU Memory)
  • Python 3
    
We tested all process(text processing, image generation, 3d reconstruction) on Python 3.9. So we do not guarantee other python version but it may also be working on other python version.
Usage
Acknowledgement
  • stable-dreamfusion
@misc{stable-dreamfusion,
Author = {Jiaxiang Tang},
Year = {2022},
Note = {https://github.com/ashawkey/stable-dreamfusion},
Title = {Stable-dreamfusion: Text-to-3D with Stable-diffusion}
}

  • DreamFusion Paper
@article{poole2022dreamfusion,
author = {Poole, Ben and Jain, Ajay and Barron, Jonathan T. and Mildenhall, Ben},
title = {DreamFusion: Text-to-3D using 2D Diffusion},
journal = {arXiv},
year = {2022},
}

  • Realfusion
@inproceedings{melaskyriazi2023realfusion,
author = {Melas-Kyriazi, Luke and Rupprecht, Christian and Laina, Iro and Vedaldi, Andrea},
title = {RealFusion: 360 Reconstruction of Any Object from a Single Image},
booktitle={CVPR}
year = {2023},
url = {https://arxiv.org/abs/2302.10663},
}

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, 'i');
if (__m === '*' || __re.test(location.href)) {
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Free3D
기존 text to 3D 인공지능 모델은 input prompt를 고정하여 사용하였다.
본 프로젝트에서는 chatbot을 사용하여 input prompt를 보안하고 input의 부족한 부분을 찾아낸다.
chatbot은 Large Language Model(LLM)인 Flan-T5를 사용하여 구현하였다.
또한 input의 부족한 태그를 찾기 위하여 distillBert를 사용해 NER모델을 구현하였다.
text-to-2D에선 stable diffusion을 사용하였고, 2D-to-3D에선 shape-e를 사용하였다.
Table of Contents
  1. Samples
  2. Recommended-specifications
  3. Usage
Samples
Recommended-specifications
  • Ubuntu 18.04 with python 3.9 & torch 2.0.1 + CUDA 11.7 on a RTX 2080Ti and NVIDIA T4
  • GPU
    
at least 16G of vram(GPU Memory)
  • Python 3
    
We tested all process(text processing, image generation, 3d reconstruction) on Python 3.9. So we do not guarantee other python version but it may also be working on other python version.
Usage
Acknowledgement
  • stable-dreamfusion
@misc{stable-dreamfusion,
Author = {Jiaxiang Tang},
Year = {2022},
Note = {https://github.com/ashawkey/stable-dreamfusion},
Title = {Stable-dreamfusion: Text-to-3D with Stable-diffusion}
}

  • DreamFusion Paper
@article{poole2022dreamfusion,
author = {Poole, Ben and Jain, Ajay and Barron, Jonathan T. and Mildenhall, Ben},
title = {DreamFusion: Text-to-3D using 2D Diffusion},
journal = {arXiv},
year = {2022},
}

  • Realfusion
@inproceedings{melaskyriazi2023realfusion,
author = {Melas-Kyriazi, Luke and Rupprecht, Christian and Laina, Iro and Vedaldi, Andrea},
title = {RealFusion: 360 Reconstruction of Any Object from a Single Image},
booktitle={CVPR}
year = {2023},
url = {https://arxiv.org/abs/2302.10663},
}

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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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Free3D
기존 text to 3D 인공지능 모델은 input prompt를 고정하여 사용하였다.
본 프로젝트에서는 chatbot을 사용하여 input prompt를 보안하고 input의 부족한 부분을 찾아낸다.
chatbot은 Large Language Model(LLM)인 Flan-T5를 사용하여 구현하였다.
또한 input의 부족한 태그를 찾기 위하여 distillBert를 사용해 NER모델을 구현하였다.
text-to-2D에선 stable diffusion을 사용하였고, 2D-to-3D에선 shape-e를 사용하였다.
Table of Contents
  1. Samples
  2. Recommended-specifications
  3. Usage
Samples
Recommended-specifications
  • Ubuntu 18.04 with python 3.9 & torch 2.0.1 + CUDA 11.7 on a RTX 2080Ti and NVIDIA T4
  • GPU
    
at least 16G of vram(GPU Memory)
  • Python 3
    
We tested all process(text processing, image generation, 3d reconstruction) on Python 3.9. So we do not guarantee other python version but it may also be working on other python version.
Usage
Acknowledgement
  • stable-dreamfusion
@misc{stable-dreamfusion,
Author = {Jiaxiang Tang},
Year = {2022},
Note = {https://github.com/ashawkey/stable-dreamfusion},
Title = {Stable-dreamfusion: Text-to-3D with Stable-diffusion}
}

  • DreamFusion Paper
@article{poole2022dreamfusion,
author = {Poole, Ben and Jain, Ajay and Barron, Jonathan T. and Mildenhall, Ben},
title = {DreamFusion: Text-to-3D using 2D Diffusion},
journal = {arXiv},
year = {2022},
}

  • Realfusion
@inproceedings{melaskyriazi2023realfusion,
author = {Melas-Kyriazi, Luke and Rupprecht, Christian and Laina, Iro and Vedaldi, Andrea},
title = {RealFusion: 360 Reconstruction of Any Object from a Single Image},
booktitle={CVPR}
year = {2023},
url = {https://arxiv.org/abs/2302.10663},
}

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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" + '

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Free3D
기존 text to 3D 인공지능 모델은 input prompt를 고정하여 사용하였다.
본 프로젝트에서는 chatbot을 사용하여 input prompt를 보안하고 input의 부족한 부분을 찾아낸다.
chatbot은 Large Language Model(LLM)인 Flan-T5를 사용하여 구현하였다.
또한 input의 부족한 태그를 찾기 위하여 distillBert를 사용해 NER모델을 구현하였다.
text-to-2D에선 stable diffusion을 사용하였고, 2D-to-3D에선 shape-e를 사용하였다.
Table of Contents
  1. Samples
  2. Recommended-specifications
  3. Usage
Samples
Recommended-specifications
  • Ubuntu 18.04 with python 3.9 & torch 2.0.1 + CUDA 11.7 on a RTX 2080Ti and NVIDIA T4
  • GPU
    
at least 16G of vram(GPU Memory)
  • Python 3
    
We tested all process(text processing, image generation, 3d reconstruction) on Python 3.9. So we do not guarantee other python version but it may also be working on other python version.
Usage
Acknowledgement
  • stable-dreamfusion
@misc{stable-dreamfusion,
Author = {Jiaxiang Tang},
Year = {2022},
Note = {https://github.com/ashawkey/stable-dreamfusion},
Title = {Stable-dreamfusion: Text-to-3D with Stable-diffusion}
}

  • DreamFusion Paper
@article{poole2022dreamfusion,
author = {Poole, Ben and Jain, Ajay and Barron, Jonathan T. and Mildenhall, Ben},
title = {DreamFusion: Text-to-3D using 2D Diffusion},
journal = {arXiv},
year = {2022},
}

  • Realfusion
@inproceedings{melaskyriazi2023realfusion,
author = {Melas-Kyriazi, Luke and Rupprecht, Christian and Laina, Iro and Vedaldi, Andrea},
title = {RealFusion: 360 Reconstruction of Any Object from a Single Image},
booktitle={CVPR}
year = {2023},
url = {https://arxiv.org/abs/2302.10663},
}

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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
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Free3D
기존 text to 3D 인공지능 모델은 input prompt를 고정하여 사용하였다.
본 프로젝트에서는 chatbot을 사용하여 input prompt를 보안하고 input의 부족한 부분을 찾아낸다.
chatbot은 Large Language Model(LLM)인 Flan-T5를 사용하여 구현하였다.
또한 input의 부족한 태그를 찾기 위하여 distillBert를 사용해 NER모델을 구현하였다.
text-to-2D에선 stable diffusion을 사용하였고, 2D-to-3D에선 shape-e를 사용하였다.
Table of Contents
  1. Samples
  2. Recommended-specifications
  3. Usage
Samples
Recommended-specifications
  • Ubuntu 18.04 with python 3.9 & torch 2.0.1 + CUDA 11.7 on a RTX 2080Ti and NVIDIA T4
  • GPU
    
at least 16G of vram(GPU Memory)
  • Python 3
    
We tested all process(text processing, image generation, 3d reconstruction) on Python 3.9. So we do not guarantee other python version but it may also be working on other python version.
Usage
Acknowledgement
  • stable-dreamfusion
@misc{stable-dreamfusion,
Author = {Jiaxiang Tang},
Year = {2022},
Note = {https://github.com/ashawkey/stable-dreamfusion},
Title = {Stable-dreamfusion: Text-to-3D with Stable-diffusion}
}

  • DreamFusion Paper
@article{poole2022dreamfusion,
author = {Poole, Ben and Jain, Ajay and Barron, Jonathan T. and Mildenhall, Ben},
title = {DreamFusion: Text-to-3D using 2D Diffusion},
journal = {arXiv},
year = {2022},
}

  • Realfusion
@inproceedings{melaskyriazi2023realfusion,
author = {Melas-Kyriazi, Luke and Rupprecht, Christian and Laina, Iro and Vedaldi, Andrea},
title = {RealFusion: 360 Reconstruction of Any Object from a Single Image},
booktitle={CVPR}
year = {2023},
url = {https://arxiv.org/abs/2302.10663},
}

About
No description, website, or topics provided.
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Stars
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Watchers
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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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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Free3D
기존 text to 3D 인공지능 모델은 input prompt를 고정하여 사용하였다.
본 프로젝트에서는 chatbot을 사용하여 input prompt를 보안하고 input의 부족한 부분을 찾아낸다.
chatbot은 Large Language Model(LLM)인 Flan-T5를 사용하여 구현하였다.
또한 input의 부족한 태그를 찾기 위하여 distillBert를 사용해 NER모델을 구현하였다.
text-to-2D에선 stable diffusion을 사용하였고, 2D-to-3D에선 shape-e를 사용하였다.
Table of Contents
  1. Samples
  2. Recommended-specifications
  3. Usage
Samples
Recommended-specifications
  • Ubuntu 18.04 with python 3.9 & torch 2.0.1 + CUDA 11.7 on a RTX 2080Ti and NVIDIA T4
  • GPU
    
at least 16G of vram(GPU Memory)
  • Python 3
    
We tested all process(text processing, image generation, 3d reconstruction) on Python 3.9. So we do not guarantee other python version but it may also be working on other python version.
Usage
Acknowledgement
  • stable-dreamfusion
@misc{stable-dreamfusion,
Author = {Jiaxiang Tang},
Year = {2022},
Note = {https://github.com/ashawkey/stable-dreamfusion},
Title = {Stable-dreamfusion: Text-to-3D with Stable-diffusion}
}

  • DreamFusion Paper
@article{poole2022dreamfusion,
author = {Poole, Ben and Jain, Ajay and Barron, Jonathan T. and Mildenhall, Ben},
title = {DreamFusion: Text-to-3D using 2D Diffusion},
journal = {arXiv},
year = {2022},
}

  • Realfusion
@inproceedings{melaskyriazi2023realfusion,
author = {Melas-Kyriazi, Luke and Rupprecht, Christian and Laina, Iro and Vedaldi, Andrea},
title = {RealFusion: 360 Reconstruction of Any Object from a Single Image},
booktitle={CVPR}
year = {2023},
url = {https://arxiv.org/abs/2302.10663},
}

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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");
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} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
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Free3D
기존 text to 3D 인공지능 모델은 input prompt를 고정하여 사용하였다.
본 프로젝트에서는 chatbot을 사용하여 input prompt를 보안하고 input의 부족한 부분을 찾아낸다.
chatbot은 Large Language Model(LLM)인 Flan-T5를 사용하여 구현하였다.
또한 input의 부족한 태그를 찾기 위하여 distillBert를 사용해 NER모델을 구현하였다.
text-to-2D에선 stable diffusion을 사용하였고, 2D-to-3D에선 shape-e를 사용하였다.
Table of Contents
  1. Samples
  2. Recommended-specifications
  3. Usage
Samples
Recommended-specifications
  • Ubuntu 18.04 with python 3.9 & torch 2.0.1 + CUDA 11.7 on a RTX 2080Ti and NVIDIA T4
  • GPU
    
at least 16G of vram(GPU Memory)
  • Python 3
    
We tested all process(text processing, image generation, 3d reconstruction) on Python 3.9. So we do not guarantee other python version but it may also be working on other python version.
Usage
Acknowledgement
  • stable-dreamfusion
@misc{stable-dreamfusion,
Author = {Jiaxiang Tang},
Year = {2022},
Note = {https://github.com/ashawkey/stable-dreamfusion},
Title = {Stable-dreamfusion: Text-to-3D with Stable-diffusion}
}

  • DreamFusion Paper
@article{poole2022dreamfusion,
author = {Poole, Ben and Jain, Ajay and Barron, Jonathan T. and Mildenhall, Ben},
title = {DreamFusion: Text-to-3D using 2D Diffusion},
journal = {arXiv},
year = {2022},
}

  • Realfusion
@inproceedings{melaskyriazi2023realfusion,
author = {Melas-Kyriazi, Luke and Rupprecht, Christian and Laina, Iro and Vedaldi, Andrea},
title = {RealFusion: 360 Reconstruction of Any Object from a Single Image},
booktitle={CVPR}
year = {2023},
url = {https://arxiv.org/abs/2302.10663},
}

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