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+# Cube: Generative AI for Advanced 3D Asset Creation and Digital Content Generation
+
+## Project Overview
+
+Cube is an advanced generative AI system designed to revolutionize 3D asset creation and digital content generation. Developed by Roblox's Foundation AI Team, this project aims to build a foundational model for 3D intelligence that empowers developers and creators to produce sophisticated 3D content with unprecedented ease.
+
+### Core Objective
+
+The primary goal of Cube is to democratize 3D content creation by providing an intelligent, text-driven system for generating high-quality 3D assets. It represents a significant step towards enabling developers to create complex 3D objects, scenes, and interactive elements through simple text prompts.
+
+### Key Features
+
+- **Text-to-Shape Generation**: Transform natural language descriptions into detailed 3D models with high fidelity and creativity
+- **Shape Tokenization**: Advanced tokenization technique that enables complex 3D asset representation and manipulation
+- **Flexible Inference**: Support for various hardware configurations, including GPUs and Apple Silicon
+- **Open-Source Accessibility**: Designed to be accessible to researchers, developers, and creators of all skill levels
+
+### Technical Capabilities
+
+- Generate 3D models from textual descriptions
+- Tokenize and reconstruct 3D shapes
+- Support for various 3D asset generation scenarios
+- Low-friction API for easy integration into existing workflows
+
+### Potential Applications
+
+- Game development
+- 3D asset creation
+- Prototyping digital objects
+- Creative design and visualization
+- Research in generative AI and 3D modeling
+
+## Getting Started, Installation, and Setup
+
+### Prerequisites
+
+- Python 3.7 or higher
+- PyTorch 2.2.2+
+- pip package manager
+
+### Installation
+
+You can install the project directly from GitHub:
+
+```bash
+pip install git+https://github.com/your-repo/cube3d.git
+```
+
+#### Optional Dependencies
+
+For additional functionality, you can install optional dependencies:
+
+```bash
+# Install MeshLab support
+pip install "cube3d[meshlab]"
+
+# Install linting tools
+pip install "cube3d[lint]"
+```
+
+### Quick Start
+
+#### Basic Usage
+
+```python
+from cube3d import generate
+
+# Example generation (placeholder - adjust based on actual API)
+model = generate.create_model()
+output = model.generate_3d_asset(prompt="A detailed boat")
+```
+
+### Development Setup
+
+1. Clone the repository:
+```bash
+git clone https://github.com/your-repo/cube3d.git
+cd cube3d
+```
+
+2. Create a virtual environment:
+```bash
+python -m venv venv
+source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
+```
+
+3. Install dependencies:
+```bash
+pip install -e .
+```
+
+### System Requirements
+
+- Recommended: CUDA-capable GPU for optimal performance
+- Minimum 16GB RAM
+- Disk space: Approximately 5GB for model weights and dependencies
+
+### Troubleshooting
+
+- Ensure you have the latest version of PyTorch compatible with your CUDA version
+- If you encounter dependency conflicts, consider using a virtual environment
+- Check `requirements.txt` for exact dependency versions if needed
+
+### Performance Notes
+
+- Model generation time varies based on hardware
+- GPU acceleration significantly improves performance
+
+## API Reference
+
+### Main Generation Functions
+
+#### `generate_mesh(engine, prompt, output_dir, output_name, resolution_base=8.0, disable_postprocess=False, top_p=None)`
+Generates a 3D mesh from a text prompt using the specified engine.
+
+- **Parameters**:
+  - `engine`: An inference engine (Engine or EngineFast)
+  - `prompt` (str): Text description for mesh generation
+  - `output_dir` (str): Directory to save the generated mesh
+  - `output_name` (str): Base filename for the output mesh
+  - `resolution_base` (float, optional): Resolution base for shape decoder. Defaults to 8.0.
+  - `disable_postprocess` (bool, optional): Skip mesh postprocessing. Defaults to False.
+  - `top_p` (float, optional): Probability threshold for token selection. Defaults to None.
+
+- **Returns**: 
+  - `str`: Path to the generated .obj mesh file
+
+- **Example**:
+```python
+from cube3d.inference.engine import Engine
+from cube3d.generate import generate_mesh
+
+engine = Engine(config_path, gpt_ckpt_path, shape_ckpt_path)
+mesh_path = generate_mesh(
+    engine, 
+    prompt="A detailed spaceship", 
+    output_dir="./outputs", 
+    output_name="spaceship"
+)
+```
+
+### Inference Engines
+
+#### `Engine(config_path, gpt_ckpt_path, shape_ckpt_path, device=None)`
+Standard inference engine for 3D mesh generation.
+
+- **Parameters**:
+  - `config_path` (str): Path to configuration YAML
+  - `gpt_ckpt_path` (str): Path to GPT checkpoint
+  - `shape_ckpt_path` (str): Path to shape encoder/decoder checkpoint
+  - `device` (torch.device, optional): Computation device. Defaults to auto-detect.
+
+#### `EngineFast(config_path, gpt_ckpt_path, shape_ckpt_path, device=None)`
+Optimized inference engine using CUDA graphs for faster generation.
+
+- **Parameters**: Same as `Engine`
+
+### Mesh Rendering
+
+#### `renderer.render_turntable(obj_path, output_dir)`
+Renders a turntable GIF animation of a 3D mesh.
+
+- **Parameters**:
+  - `obj_path` (str): Path to the .obj mesh file
+  - `output_dir` (str): Directory to save the output GIF
+
+- **Returns**: 
+  - `str`: Path to the generated GIF file
+
+### Mesh Postprocessing Utilities
+
+These utilities are available if PyMeshLab is installed:
+
+#### `create_pymeshset(vertices, faces)`
+Creates a PyMeshLab mesh set from vertices and faces.
+
+#### `postprocess_mesh(mesh_set, target_face_num, output_path)`
+Simplifies and cleans the mesh to a target number of faces.
+
+#### `save_mesh(mesh_set, output_path)`
+Saves a PyMeshLab mesh set to a file.
+
+### Constants
+
+- `PYMESHLAB_AVAILABLE` (bool): Indicates whether PyMeshLab is installed for advanced mesh processing.
+
+### Usage Notes
+- Ensure required checkpoints and configuration files are available
+- PyMeshLab is recommended but optional for mesh processing
+- CUDA is supported for faster inference on compatible hardware
+
+## Project Structure
+
+The project is organized into several key directories and files to support its 3D rendering and machine learning capabilities:
+
+### Main Package Structure
+- `cube3d/`: Primary package directory containing the core implementation
+    - `__init__.py`: Package initialization file
+    - `generate.py`: Core generation logic
+    - `colab_cube3d.ipynb`: Jupyter notebook for Colab integration
+    - `configs/`: Configuration management
+        - `open_model.yaml`: Model configuration file
+    
+### Submodules
+- `cube3d/inference/`: Inference-related functionality
+    - `engine.py`: Core inference engine
+    - `logits_postprocesses.py`: Logits post-processing utilities
+    - `utils.py`: Utility functions for inference
+
+- `cube3d/model/`: Machine learning model implementations
+    - `autoencoder/`: Autoencoder-related modules
+        - `embedder.py`: Embedding logic
+        - `grid.py`: Grid-related operations
+        - `one_d_autoencoder.py`: One-dimensional autoencoder
+        - `spherical_vq.py`: Spherical vector quantization
+
+    - `gpt/`: GPT model implementations
+        - `dual_stream_roformer.py`: Dual-stream RoFormer model
+
+    - `transformers/`: Transformer-related modules
+        - `attention.py`: Attention mechanisms
+        - `cache.py`: Caching utilities
+        - `dual_stream_attention.py`: Dual-stream attention implementation
+        - `norm.py`: Normalization layers
+        - `roformer.py`: RoFormer implementation
+        - `rope.py`: Rotary Position Embedding
+
+- `cube3d/renderer/`: Rendering utilities
+    - `blender_script.py`: Blender rendering script
+    - `renderer.py`: Rendering engine
+
+### Additional Directories
+- `examples/`: Sample data and example files
+    - Contains `.obj` and `.gif` files for different objects
+    - `prompts.json`: Prompt configurations
+
+- `resources/`: Static resources
+    - Contains various image and GIF files
+
+### Project Configuration and Setup
+- `pyproject.toml`: Project build system configuration
+- `setup.py`: Package installation and setup script
+
+### Documentation and Metadata
+- `README.md`: Project documentation
+- `LICENSE`: Project licensing information
+- `SECURITY.md`: Security policy
+- `.github/`: GitHub-specific configurations
+    - Issue and pull request templates
+
+## Technologies Used
+
+### Programming Languages
+- Python 3.10+
+
+### Machine Learning and Deep Learning
+- PyTorch (v2.2.2+): Primary deep learning framework
+- Hugging Face Transformers: Transformer model architectures
+- Accelerate: Distributed training and inference library
+- NumPy: Numerical computing library
+
+### 3D Graphics and Rendering
+- Trimesh: 3D mesh processing and manipulation
+- Warp-lang: High-performance graphics and simulation library
+- Blender (via scripting): 3D rendering and visualization
+
+### Configuration and Utilities
+- OmegaConf: Configuration management
+- tqdm: Progress bar and iteration tracking
+- Scikit-image: Image processing utilities
+
+### Development and Tooling
+- Setuptools: Python package management
+- Ruff: Python linting and code quality tool
+- Hugging Face CLI: Model and dataset management
+
+### Optional Dependencies
+- PyMeshLab: Advanced mesh processing (optional)
+
+## Additional Notes
+
+### Model Capabilities and Limitations
+
+The Cube 3D model is designed for generative 3D asset creation with text-to-shape capabilities. It's important to understand the current scope and constraints:
+
+- **Generation Scope**: Primarily focused on creating individual 3D objects from text prompts
+- **Resolution Flexibility**: Users can adjust output resolution using the `--resolution-base` flag, balancing between detail and computational efficiency
+- **Rendering Requirements**: Turntable GIF generation requires Blender (version >= 4.3) installed in the system PATH
+
+### Experimental Features
+
+The project includes experimental capabilities that are actively being developed:
+
+- Bounding box conditioning for shape generation
+- Preliminary scene generation techniques
+- Shape tokenization and reconstruction workflows
+
+### Performance Considerations
+
+- **Recommended Hardware**: 
+  - GPU with 24GB VRAM for fast inference
+  - Minimum 16GB VRAM for standard inference
+  - Tested on Nvidia H100, A100, GeForce 3080, and Apple Silicon M2-4 Chips
+
+### Future Development
+
+The Cube 3D project represents an initial step towards comprehensive 3D intelligence, with ongoing research focusing on:
+
+- Enhanced 3D object generation
+- More sophisticated scene creation
+- Improved text-to-3D conversion techniques
+
+### Responsible Use
+
+As an experimental AI technology, users are encouraged to:
+
+- Verify generated assets for accuracy and appropriateness
+- Use the model ethically and in compliance with relevant guidelines
+- Understand that generated outputs may vary in quality and fidelity
+
+## Contributing
+
+We welcome contributions to the project! To ensure a smooth collaboration, please follow these guidelines:
+
+### Pull Request Process
+1. Fork the repository and create your branch from `main`.
+2. Ensure any new code follows the project's coding standards.
+3. Update relevant documentation to reflect your changes.
+4. Your pull request should include:
+   - A clear summary of changes
+   - Context and rationale for modifications
+   - Testing instructions
+   - Any potential breaking changes or new dependencies
+
+### Code Style
+- Use Python 3.7+ compatible code
+- Follow standard Python formatting conventions
+- Use type hints where appropriate
+
+### Development Setup
+- Minimum Python version: 3.7
+- Install development dependencies with `pip install .[lint]`
+
+### Linting
+- The project uses `ruff` for code linting (version 0.9.10)
+- Run linter checks before submitting your pull request
+
+### Testing
+- Ensure all tests pass before submitting a pull request
+- Add tests for new functionality
+- Maintain or improve overall test coverage
+
+### Reporting Issues
+- Use the GitHub issue templates for bug reports, feature requests, or documentation improvements
+- Provide clear, reproducible steps for any bugs discovered
+
+### Security
+If you discover a security vulnerability:
+- Do NOT create a public issue
+- Report vulnerabilities through the HackerOne bug bounty program: https://hackerone.com/roblox
+
+### Code of Conduct
+Be respectful, inclusive, and considerate of other contributors. Harassment and discrimination are not tolerated.
+
+### Ownership
+This project is maintained by @Roblox/aicube. All contributions are subject to review by the core team.
+
+## License
+
+The project is licensed under the **Cube3D Research-Only RAIL-MS License**. 
+
+### Key License Highlights
+
+- This is a research-only license with specific use restrictions
+- The license applies to Cube3d-v0.1 and related inference code
+- Usage is strictly limited to academic or research purposes
+
+### Important Restrictions
+
+The license includes comprehensive use restrictions, including:
+- Prohibitions on discrimination
+- Intellectual property protections
+- Restrictions on generating harmful or unlawful content
+- Limitations on military, law enforcement, and health-related applications
+
+### Full License Details
+
+For the complete and legally binding license terms, please refer to the [LICENSE](LICENSE) file in the repository. Users must carefully review and comply with all terms before using this software.
+
+#### Disclaimer
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND. The licensors are not liable for damages arising from its use.
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