| # 🌀 Spatial Diffusion |
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| **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. |
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| ## 🌐 Model Highlights |
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| - **Cubemap Representation** |
| Generates six cube faces to represent the entire spherical environment, maintaining consistent spatial alignment. |
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| - **Diffusion-Based Generation** |
| Uses a diffusion process to progressively refine spatial details and structure, producing high-quality and coherent outputs. |
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| - **360° View Synthesis** |
| Capable of producing panoramas suitable for virtual reality, robotics, and simulation environments. |
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| ## 🚀 Intended Applications |
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| - Virtual Reality (VR) scene generation |
| - Environmental simulation and reconstruction |
| - Robotics & autonomous navigation (spatial awareness) |
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| ## ⚠️ Limitations |
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| - 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. |
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| ## 📄 Citation |
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| If you use this model in your research or application, please cite: |
| Spatial Diffusion: Cubemap-Based Generation of Spatial Panoramas, [Ziming He], 2025. |
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