First 3D Generative Deconstruction Model PartCrafter Debuts, Gains 2K Stars on GitHub
# PartCrafter: Structured 3D Mesh Generation from a Single Image
Creating editable 3D models from a single image is one of the **major challenges in computer graphics**.
Traditional 3D generative models tend to produce holistic “black-box” assets—making fine-tuning individual components nearly impossible.
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## Introduction
Researchers from **Peking University**, **ByteDance**, and **Carnegie Mellon University** have jointly introduced **PartCrafter** — an **innovative structured generation model** that can:
- Create **complex 3D meshes** from a single 2D image
- Compose them of **multiple semantically meaningful**, independently controllable parts
**Significance:**
- **Improved controllability** and **interpretability** in 3D generation
- **Accepted at NeurIPS 2025**
- Earned **2000+ GitHub Stars**
**Official Resources:**
- **Paper Title:** *PartCrafter: Structured 3D Mesh Generation via Compositional Latent Diffusion Transformers*
- **Project Page:** [https://wgsxm.github.io/projects/partcrafter/](https://wgsxm.github.io/projects/partcrafter/)
- **Paper Link:** [https://arxiv.org/abs/2506.05573](https://arxiv.org/abs/2506.05573)
- **Code Link:** [https://github.com/wgsxm/PartCrafter](https://github.com/wgsxm/PartCrafter)
**Key Feature:** Generates multi-part 3D mesh representations **in seconds** without segmentation steps.
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## Research Background
### Problem with Current Approaches
- Mainstream 3D AIGC models treat objects as **indivisible wholes**
- This makes **independent movement, rotation, or replacement** of components impossible
### Traditional Workflow:
1. **Segmentation**: Identify parts in 2D images
2. **Reconstruction**: Generate each part’s 3D form independently
**Drawbacks:**
- **Slow**: Can take 20+ minutes
- **Error-prone**: Mistakes in segmentation propagate irreversibly
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## PartCrafter Solution
**Goal:**
An **end-to-end structured 3D generation system** capable of:
- **Direct composite mesh generation**
- **Multiple independent components**
- **Single-image input**
**Benefits:**
- Quality generation at **second-level speeds**
- **Unprecedented controllability**
- Modular content creation workflows
*PartCrafter Network Architecture*
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## Methodology
### 1. Compositional Latent Space
**Key Idea:**
Each distinct object part gets **its own set of latent tokens** — enabling decomposition into truly independent components.
**Enhancement:**
**Part Identity Embedding** — a unique learnable vector for each part group
- Works like an “ID card” for parts
- Improves semantic awareness and part recognition
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### 2. Local-Global Denoising Transformer
**Challenge:** Independent part generation often creates distortion in layout or proportion.
**Solution:** Two synchronized attention branches:
- **Local Branch (Local Attention Blocks):**
- Focuses on geometry and detail **within each part**
- Ensures individual components maintain structural integrity
- **Global Branch (Global Attention Blocks):**
- Coordinates spatial relationships **between parts**
- Maintains overall harmony and proportion
**Generation Process:**
Image conditions guide the production of part-separable, structurally consistent 3D models.
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## Dataset Construction
**Need:**
Large-scale 3D datasets with **part-level annotations**
**Problem:**
Popular datasets (Objaverse, ShapeNet, ABO) lack fine-grained part labels.
**PartCrafter Dataset:**
- ~130,000 3D objects
- ~100,000 with accurate multi-part annotations
- Final set: **50,000 high-quality objects**, **300,000 individual parts**
- Quality criteria: texture fidelity, reasonable part count, high IoU
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## Experimental Results
### 1. Quantitative Results
#### 1.1 Object Datasets
- Compared with HoloPart
- Generates high-fidelity meshes in ~34 seconds
- HoloPart slower, requires segmentation, yields lower accuracy
#### 1.2 Scene Datasets
- Compared with MIDI model on occlusion-heavy 3D-Front dataset subset
- PartCrafter maintains quality without segmentation masks
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### 2. Qualitative Results
**Highlights:**
- High geometric fidelity and coherent part integration
- Adjustable part segmentation granularity (e.g., 3 parts vs. 8 parts)
**Applications:**
- Industrial design
- VR
- Game asset creation
**Platform Synergy:**
**[AiToEarn](https://aitoearn.ai/)** — complements 3D generation by enabling:
- Global publishing
- Analytics
- Monetization
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#### 2.1 3D Object Reconstruction
#### 2.2 3D Scene Reconstruction
#### 2.3 User-Specified Part Granularity
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## Conclusion & Outlook
PartCrafter advances 3D generation from **holistic** to **structured** modeling.
**Advantages:**
- Editable parts
- Broader application scope
- Faster pipelines
**Future Impact:**
- Gaming, VR, industrial design
- Modular, hierarchical 3D world models
**Workflow Integration:**
High-quality meshes can flow directly into:
- Rendering
- Animation
- Game development
**Ecosystem Support:**
Tools like **[AiToEarn](https://aitoearn.ai/)** and resources like **[AiToEarn开源地址](https://github.com/yikart/AiToEarn)** & **[AiToEarn文档](https://docs.aitoearn.ai/)** enhance **cross-platform publishing** and monetization — accelerating the path from content creation to market reach.
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