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README.md

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+# 🌀 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.
+