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AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer (ICLR 2026)

This is the official code for AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer.

Abstract

The digital industry demands high-quality, diverse modular 3D assets, especially for user-generated content (UGC). In this work, we introduce AssetFormer, an autoregressive Transformer-based model designed to generate modular 3D assets from textual descriptions. Our pilot study leverages real-world modular assets collected from online platforms. AssetFormer tackles the challenge of creating assets composed of primitives that adhere to constrained design parameters for various applications. By innovatively adapting module sequencing and decoding techniques inspired by language models, our approach enhances asset generation quality through autoregressive modeling. Initial results indicate the effectiveness of AssetFormer in streamlining asset creation for professional development and UGC scenarios. This work presents a flexible framework extendable to various types of modular 3D assets, contributing to the broader field of 3D content generation.

Installation & Preparation

  1. Clone this repository and install packages:

    git clone https://github.com/Advocate99/AssetFormer.git
conda create -n assetformer python=3.12
conda activate assetformer
pip install -r requirements.txt

  2. Download flan-t5-xl models from flan-t5-xl and put into the folder of ./pretrained_models/t5-ckpt/:

    huggingface-cli download google/flan-t5-xl --local-dir ./pretrained_models/t5-ckpt/flan-t5-xl

  3. Download the pretrained model from ltzhu/AssetFormer and put into the folder of ./pretrained_models/:

    huggingface-cli download ltzhu/AssetFormer --local-dir ./pretrained_models

Inference

  1. Run the following command to sample 3D assets json files:

    python sample.py --gpt-ckpt ./pretrained_models/inference_model.pt

  2. Use blender script to render the 3D assets in blender with the modular fbx files. The script is located in ./blender_script/.

Citation

If you find our work useful, please kindly cite as:

@article{zhu2026assetformer,
title={AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer},
author={Zhu, Lingting and Qian, Shengju and Fan, Haidi and Dong, Jiayu and Jin, Zhenchao and Zhou, Siwei and Dong, Gen and Wang, Xin and Yu, Lequan},
journal={arXiv preprint arXiv:2602.12100},
year={2026}
}

Acknowledgement

  • The codebase is developed based on LlamaGen.

About

[ICLR'2026] AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer

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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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AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer (ICLR 2026)
This is the official code for AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer.
Abstract
The digital industry demands high-quality, diverse modular 3D assets, especially for user-generated content (UGC). In this work, we introduce AssetFormer, an autoregressive Transformer-based model designed to generate modular 3D assets from textual descriptions. Our pilot study leverages real-world modular assets collected from online platforms. AssetFormer tackles the challenge of creating assets composed of primitives that adhere to constrained design parameters for various applications. By innovatively adapting module sequencing and decoding techniques inspired by language models, our approach enhances asset generation quality through autoregressive modeling. Initial results indicate the effectiveness of AssetFormer in streamlining asset creation for professional development and UGC scenarios. This work presents a flexible framework extendable to various types of modular 3D assets, contributing to the broader field of 3D content generation.
Installation & Preparation
  1. Clone this repository and install packages:
    git clone https://github.com/Advocate99/AssetFormer.git
conda create -n assetformer python=3.12
conda activate assetformer
pip install -r requirements.txt

  2. Download flan-t5-xl models from flan-t5-xl and put into the folder of ./pretrained_models/t5-ckpt/:
    huggingface-cli download google/flan-t5-xl --local-dir ./pretrained_models/t5-ckpt/flan-t5-xl

  3. Download the pretrained model from ltzhu/AssetFormer and put into the folder of ./pretrained_models/:
    huggingface-cli download ltzhu/AssetFormer --local-dir ./pretrained_models

Inference
  1. Run the following command to sample 3D assets json files:
    python sample.py --gpt-ckpt ./pretrained_models/inference_model.pt

  2. Use blender script to render the 3D assets in blender with the modular fbx files. The script is located in ./blender_script/.
Citation
If you find our work useful, please kindly cite as:
@article{zhu2026assetformer,
title={AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer},
author={Zhu, Lingting and Qian, Shengju and Fan, Haidi and Dong, Jiayu and Jin, Zhenchao and Zhou, Siwei and Dong, Gen and Wang, Xin and Yu, Lequan},
journal={arXiv preprint arXiv:2602.12100},
year={2026}
}

Acknowledgement
  • The codebase is developed based on LlamaGen.
About
[ICLR'2026] AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer
Resources
Readme
Activity
Stars
40 stars
Watchers
2 watching
Forks
1 fork
Report repository
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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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AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer (ICLR 2026)
This is the official code for AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer.
Abstract
The digital industry demands high-quality, diverse modular 3D assets, especially for user-generated content (UGC). In this work, we introduce AssetFormer, an autoregressive Transformer-based model designed to generate modular 3D assets from textual descriptions. Our pilot study leverages real-world modular assets collected from online platforms. AssetFormer tackles the challenge of creating assets composed of primitives that adhere to constrained design parameters for various applications. By innovatively adapting module sequencing and decoding techniques inspired by language models, our approach enhances asset generation quality through autoregressive modeling. Initial results indicate the effectiveness of AssetFormer in streamlining asset creation for professional development and UGC scenarios. This work presents a flexible framework extendable to various types of modular 3D assets, contributing to the broader field of 3D content generation.
Installation & Preparation
  1. Clone this repository and install packages:
    git clone https://github.com/Advocate99/AssetFormer.git
conda create -n assetformer python=3.12
conda activate assetformer
pip install -r requirements.txt

  2. Download flan-t5-xl models from flan-t5-xl and put into the folder of ./pretrained_models/t5-ckpt/:
    huggingface-cli download google/flan-t5-xl --local-dir ./pretrained_models/t5-ckpt/flan-t5-xl

  3. Download the pretrained model from ltzhu/AssetFormer and put into the folder of ./pretrained_models/:
    huggingface-cli download ltzhu/AssetFormer --local-dir ./pretrained_models

Inference
  1. Run the following command to sample 3D assets json files:
    python sample.py --gpt-ckpt ./pretrained_models/inference_model.pt

  2. Use blender script to render the 3D assets in blender with the modular fbx files. The script is located in ./blender_script/.
Citation
If you find our work useful, please kindly cite as:
@article{zhu2026assetformer,
title={AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer},
author={Zhu, Lingting and Qian, Shengju and Fan, Haidi and Dong, Jiayu and Jin, Zhenchao and Zhou, Siwei and Dong, Gen and Wang, Xin and Yu, Lequan},
journal={arXiv preprint arXiv:2602.12100},
year={2026}
}

Acknowledgement
  • The codebase is developed based on LlamaGen.
About
[ICLR'2026] AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer
Resources
Readme
Activity
Stars
40 stars
Watchers
2 watching
Forks
1 fork
Report repository
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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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AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer (ICLR 2026)
This is the official code for AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer.
Abstract
The digital industry demands high-quality, diverse modular 3D assets, especially for user-generated content (UGC). In this work, we introduce AssetFormer, an autoregressive Transformer-based model designed to generate modular 3D assets from textual descriptions. Our pilot study leverages real-world modular assets collected from online platforms. AssetFormer tackles the challenge of creating assets composed of primitives that adhere to constrained design parameters for various applications. By innovatively adapting module sequencing and decoding techniques inspired by language models, our approach enhances asset generation quality through autoregressive modeling. Initial results indicate the effectiveness of AssetFormer in streamlining asset creation for professional development and UGC scenarios. This work presents a flexible framework extendable to various types of modular 3D assets, contributing to the broader field of 3D content generation.
Installation & Preparation
  1. Clone this repository and install packages:
    git clone https://github.com/Advocate99/AssetFormer.git
conda create -n assetformer python=3.12
conda activate assetformer
pip install -r requirements.txt

  2. Download flan-t5-xl models from flan-t5-xl and put into the folder of ./pretrained_models/t5-ckpt/:
    huggingface-cli download google/flan-t5-xl --local-dir ./pretrained_models/t5-ckpt/flan-t5-xl

  3. Download the pretrained model from ltzhu/AssetFormer and put into the folder of ./pretrained_models/:
    huggingface-cli download ltzhu/AssetFormer --local-dir ./pretrained_models

Inference
  1. Run the following command to sample 3D assets json files:
    python sample.py --gpt-ckpt ./pretrained_models/inference_model.pt

  2. Use blender script to render the 3D assets in blender with the modular fbx files. The script is located in ./blender_script/.
Citation
If you find our work useful, please kindly cite as:
@article{zhu2026assetformer,
title={AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer},
author={Zhu, Lingting and Qian, Shengju and Fan, Haidi and Dong, Jiayu and Jin, Zhenchao and Zhou, Siwei and Dong, Gen and Wang, Xin and Yu, Lequan},
journal={arXiv preprint arXiv:2602.12100},
year={2026}
}

Acknowledgement
  • The codebase is developed based on LlamaGen.
About
[ICLR'2026] AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer
Resources
Readme
Activity
Stars
40 stars
Watchers
2 watching
Forks
1 fork
Report repository
Releases
Packages
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" + '

Skip to content
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AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer (ICLR 2026)
This is the official code for AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer.
Abstract
The digital industry demands high-quality, diverse modular 3D assets, especially for user-generated content (UGC). In this work, we introduce AssetFormer, an autoregressive Transformer-based model designed to generate modular 3D assets from textual descriptions. Our pilot study leverages real-world modular assets collected from online platforms. AssetFormer tackles the challenge of creating assets composed of primitives that adhere to constrained design parameters for various applications. By innovatively adapting module sequencing and decoding techniques inspired by language models, our approach enhances asset generation quality through autoregressive modeling. Initial results indicate the effectiveness of AssetFormer in streamlining asset creation for professional development and UGC scenarios. This work presents a flexible framework extendable to various types of modular 3D assets, contributing to the broader field of 3D content generation.
Installation & Preparation
  1. Clone this repository and install packages:
    git clone https://github.com/Advocate99/AssetFormer.git
conda create -n assetformer python=3.12
conda activate assetformer
pip install -r requirements.txt

  2. Download flan-t5-xl models from flan-t5-xl and put into the folder of ./pretrained_models/t5-ckpt/:
    huggingface-cli download google/flan-t5-xl --local-dir ./pretrained_models/t5-ckpt/flan-t5-xl

  3. Download the pretrained model from ltzhu/AssetFormer and put into the folder of ./pretrained_models/:
    huggingface-cli download ltzhu/AssetFormer --local-dir ./pretrained_models

Inference
  1. Run the following command to sample 3D assets json files:
    python sample.py --gpt-ckpt ./pretrained_models/inference_model.pt

  2. Use blender script to render the 3D assets in blender with the modular fbx files. The script is located in ./blender_script/.
Citation
If you find our work useful, please kindly cite as:
@article{zhu2026assetformer,
title={AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer},
author={Zhu, Lingting and Qian, Shengju and Fan, Haidi and Dong, Jiayu and Jin, Zhenchao and Zhou, Siwei and Dong, Gen and Wang, Xin and Yu, Lequan},
journal={arXiv preprint arXiv:2602.12100},
year={2026}
}

Acknowledgement
  • The codebase is developed based on LlamaGen.
About
[ICLR'2026] AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer
Resources
Readme
Activity
Stars
40 stars
Watchers
2 watching
Forks
1 fork
Report repository
Releases
Packages
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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AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer (ICLR 2026)
This is the official code for AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer.
Abstract
The digital industry demands high-quality, diverse modular 3D assets, especially for user-generated content (UGC). In this work, we introduce AssetFormer, an autoregressive Transformer-based model designed to generate modular 3D assets from textual descriptions. Our pilot study leverages real-world modular assets collected from online platforms. AssetFormer tackles the challenge of creating assets composed of primitives that adhere to constrained design parameters for various applications. By innovatively adapting module sequencing and decoding techniques inspired by language models, our approach enhances asset generation quality through autoregressive modeling. Initial results indicate the effectiveness of AssetFormer in streamlining asset creation for professional development and UGC scenarios. This work presents a flexible framework extendable to various types of modular 3D assets, contributing to the broader field of 3D content generation.
Installation & Preparation
  1. Clone this repository and install packages:
    git clone https://github.com/Advocate99/AssetFormer.git
conda create -n assetformer python=3.12
conda activate assetformer
pip install -r requirements.txt

  2. Download flan-t5-xl models from flan-t5-xl and put into the folder of ./pretrained_models/t5-ckpt/:
    huggingface-cli download google/flan-t5-xl --local-dir ./pretrained_models/t5-ckpt/flan-t5-xl

  3. Download the pretrained model from ltzhu/AssetFormer and put into the folder of ./pretrained_models/:
    huggingface-cli download ltzhu/AssetFormer --local-dir ./pretrained_models

Inference
  1. Run the following command to sample 3D assets json files:
    python sample.py --gpt-ckpt ./pretrained_models/inference_model.pt

  2. Use blender script to render the 3D assets in blender with the modular fbx files. The script is located in ./blender_script/.
Citation
If you find our work useful, please kindly cite as:
@article{zhu2026assetformer,
title={AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer},
author={Zhu, Lingting and Qian, Shengju and Fan, Haidi and Dong, Jiayu and Jin, Zhenchao and Zhou, Siwei and Dong, Gen and Wang, Xin and Yu, Lequan},
journal={arXiv preprint arXiv:2602.12100},
year={2026}
}

Acknowledgement
  • The codebase is developed based on LlamaGen.
About
[ICLR'2026] AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer
Resources
Readme
Activity
Stars
40 stars
Watchers
2 watching
Forks
1 fork
Report repository
Releases
Packages
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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AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer (ICLR 2026)
This is the official code for AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer.
Abstract
The digital industry demands high-quality, diverse modular 3D assets, especially for user-generated content (UGC). In this work, we introduce AssetFormer, an autoregressive Transformer-based model designed to generate modular 3D assets from textual descriptions. Our pilot study leverages real-world modular assets collected from online platforms. AssetFormer tackles the challenge of creating assets composed of primitives that adhere to constrained design parameters for various applications. By innovatively adapting module sequencing and decoding techniques inspired by language models, our approach enhances asset generation quality through autoregressive modeling. Initial results indicate the effectiveness of AssetFormer in streamlining asset creation for professional development and UGC scenarios. This work presents a flexible framework extendable to various types of modular 3D assets, contributing to the broader field of 3D content generation.
Installation & Preparation
  1. Clone this repository and install packages:
    git clone https://github.com/Advocate99/AssetFormer.git
conda create -n assetformer python=3.12
conda activate assetformer
pip install -r requirements.txt

  2. Download flan-t5-xl models from flan-t5-xl and put into the folder of ./pretrained_models/t5-ckpt/:
    huggingface-cli download google/flan-t5-xl --local-dir ./pretrained_models/t5-ckpt/flan-t5-xl

  3. Download the pretrained model from ltzhu/AssetFormer and put into the folder of ./pretrained_models/:
    huggingface-cli download ltzhu/AssetFormer --local-dir ./pretrained_models

Inference
  1. Run the following command to sample 3D assets json files:
    python sample.py --gpt-ckpt ./pretrained_models/inference_model.pt

  2. Use blender script to render the 3D assets in blender with the modular fbx files. The script is located in ./blender_script/.
Citation
If you find our work useful, please kindly cite as:
@article{zhu2026assetformer,
title={AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer},
author={Zhu, Lingting and Qian, Shengju and Fan, Haidi and Dong, Jiayu and Jin, Zhenchao and Zhou, Siwei and Dong, Gen and Wang, Xin and Yu, Lequan},
journal={arXiv preprint arXiv:2602.12100},
year={2026}
}

Acknowledgement
  • The codebase is developed based on LlamaGen.
About
[ICLR'2026] AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer
Resources
Readme
Activity
Stars
40 stars
Watchers
2 watching
Forks
1 fork
Report repository
Releases
Packages
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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AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer (ICLR 2026)
This is the official code for AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer.
Abstract
The digital industry demands high-quality, diverse modular 3D assets, especially for user-generated content (UGC). In this work, we introduce AssetFormer, an autoregressive Transformer-based model designed to generate modular 3D assets from textual descriptions. Our pilot study leverages real-world modular assets collected from online platforms. AssetFormer tackles the challenge of creating assets composed of primitives that adhere to constrained design parameters for various applications. By innovatively adapting module sequencing and decoding techniques inspired by language models, our approach enhances asset generation quality through autoregressive modeling. Initial results indicate the effectiveness of AssetFormer in streamlining asset creation for professional development and UGC scenarios. This work presents a flexible framework extendable to various types of modular 3D assets, contributing to the broader field of 3D content generation.
Installation & Preparation
  1. Clone this repository and install packages:
    git clone https://github.com/Advocate99/AssetFormer.git
conda create -n assetformer python=3.12
conda activate assetformer
pip install -r requirements.txt

  2. Download flan-t5-xl models from flan-t5-xl and put into the folder of ./pretrained_models/t5-ckpt/:
    huggingface-cli download google/flan-t5-xl --local-dir ./pretrained_models/t5-ckpt/flan-t5-xl

  3. Download the pretrained model from ltzhu/AssetFormer and put into the folder of ./pretrained_models/:
    huggingface-cli download ltzhu/AssetFormer --local-dir ./pretrained_models

Inference
  1. Run the following command to sample 3D assets json files:
    python sample.py --gpt-ckpt ./pretrained_models/inference_model.pt

  2. Use blender script to render the 3D assets in blender with the modular fbx files. The script is located in ./blender_script/.
Citation
If you find our work useful, please kindly cite as:
@article{zhu2026assetformer,
title={AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer},
author={Zhu, Lingting and Qian, Shengju and Fan, Haidi and Dong, Jiayu and Jin, Zhenchao and Zhou, Siwei and Dong, Gen and Wang, Xin and Yu, Lequan},
journal={arXiv preprint arXiv:2602.12100},
year={2026}
}

Acknowledgement
  • The codebase is developed based on LlamaGen.
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[ICLR'2026] AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer
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