# 🌀 Spatial Diffusion

**Spatial Diffusion** is a generative model for synthesizing **spatial panoramas** based on a **cubemap representation**. By generating six orthogonal cube faces (front, back, left, right, top, bottom), the model constructs a complete and spatially consistent 360° view of a scene. This cubemap-based approach ensures geometric coherence and enables immersive scene generation for various downstream applications.

## 🌐 Model Highlights

- **Cubemap Representation**  
  Generates six cube faces to represent the entire spherical environment, maintaining consistent spatial alignment.

- **Diffusion-Based Generation**  
  Uses a diffusion process to progressively refine spatial details and structure, producing high-quality and coherent outputs.

- **360° View Synthesis**  
  Capable of producing panoramas suitable for virtual reality, robotics, and simulation environments.

## 🚀 Intended Applications

- Virtual Reality (VR) scene generation  
- Environmental simulation and reconstruction  
- Robotics & autonomous navigation (spatial awareness)

## ⚠️ Limitations

- Performance may drop in scenes with non-Euclidean geometry or extreme occlusions.  
- Post-processing may be required for equirectangular projection if not viewed via cubemap renderers.  
- May not generalize well outside the distribution of the training dataset.

## 📄 Citation

If you use this model in your research or application, please cite:
Spatial Diffusion: Cubemap-Based Generation of Spatial Panoramas, [Ziming He], 2025.