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README.md

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+# PrimitiveAnything: Human-Crafted 3D Primitive Assembly Generation with Auto-Regressive Transformer
+
+<a href="https://primitiveanything.github.io/"><img src="https://img.shields.io/static/v1?label=Project%20Page&message=Github&color=blue&logo=github-pages"></a>&ensp;<a href="#"><img src="https://img.shields.io/badge/ArXiv-250x.xxxxx-brightgreen"></a>&ensp;<a href="https://huggingface.co/hyz317/PrimitiveAnything"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Model_Card-Huggingface-orange"></a>&ensp;<a href="https://huggingface.co/spaces/hyz317/PrimitiveAnything"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Gradio%20Demo-Huggingface-orange"></a>
+
+<img src="./assets/teaser.jpg" width="100%">
+
+
+## 🔥 Updates
+
+**[2025/05/07]** test dataset, code, pretrained checkpoints and Gradio demo are released!
+
+
+
+## 🔍 Table of Contents
+
+- [⚙️ Deployment](#deployment)
+- [🖥️ Run PrimitiveAnything](#run-pa)
+- [📝 Citation](#citation)
+
+
+
+<a name="deployment"></a>
+
+## ⚙️ Deployment
+
+Set up a Python environment and install the required packages:
+
+```bash
+conda create -n primitiveanything python=3.9 -y
+conda activate primitiveanything
+
+# Install torch, torchvision based on your machine configuration
+pip install torch==2.1.0 torchvision==0.16.0 --index-url https://download.pytorch.org/whl/cu118
+
+# Install other dependencies
+pip install -r requirements.txt
+```
+
+Then download data and pretrained weights:
+
+1. **Our Model Weights**: 
+   Download from our 🤗 Hugging Face repository ([download here](https://huggingface.co/hyz317/PrimitiveAnything)) and place them in `./ckpt/`.
+
+2. **Michelangelo’s Point Cloud Encoder**: 
+   Download weights from [Michelangelo’s Hugging Face repo](https://huggingface.co/Maikou/Michelangelo/tree/main/checkpoints/aligned_shape_latents) and save them to `./ckpt/`.
+
+3. **Demo and test data**:
+
+   Download from this [Google Drive link](https://drive.google.com/file/d/1FZZjk0OvzETD5j4OODEghS_YppcS_ZbM/view?usp=sharing), then decompress the files into `./data/`.
+
+After downloading and organizing the files, your project directory should look like this:
+
+```
+- data/
+    ├── basic_shapes_norm/
+    ├── basic_shapes_norm_pc10000/
+    ├── demo_glb/                   # Demo files in GLB format
+    └── test_pc/                    # Test point cloud data
+- ckpt/
+    ├── mesh-transformer.ckpt.60.pt # Our model checkpoint
+    └── shapevae-256.ckpt           # Michelangelo ShapeVAE checkpoint
+```
+
+
+
+<a name="run-pa"></a>
+
+## 🖥️ Run PrimitiveAnything
+
+### Demo
+
+```bash
+python demo.py --input ./data/demo_glb --log_path ./results/demo
+```
+
+**Notes:**
+
+- `--input` accepts either:
+  - Any standard 3D file (GLB, OBJ, etc.)
+  - A directory containing multiple 3D files
+- For optimal results with fine structures, we automatically apply marching cubes and dilation operations (which differs from testing and evaluation). This prevents quality degradation in thin areas.
+
+### Testing and Evaluation
+
+```bash
+# Autoregressive generation
+python infer.py
+
+# Sample point clouds from predictions  
+python sample.py
+
+# Calculate evaluation metrics
+python eval.py
+```
+
+
+
+<a name="citation"></a>
+
+## 📝 Citation
+
+If you find our work useful, please kindly cite:
+
+```
+@article{ye2025primitiveanything,
+  title={PrimitiveAnything: Human-Crafted 3D Primitive Assembly Generation with Auto-Regressive Transformer},
+  author={Ye, Jingwen and He, Yuze and Zhou, Yanning and Zhu, Yiqin and Xiao, Kaiwen and Liu, Yong-Jin and Yang, Wei and Han, Xiao},
+  journal={arXiv preprint arXiv:250x.xxxxx},
+  year={2025}
+}
+```
+