Summary of Dreamscene360: Unconstrained Text-to-3d Scene Generation with Panoramic Gaussian Splatting, by Shijie Zhou et al.
DreamScene360: Unconstrained Text-to-3D Scene Generation with Panoramic Gaussian Splatting
by Shijie Zhou, Zhiwen Fan, Dejia Xu, Haoran Chang, Pradyumna Chari, Tejas Bharadwaj, Suya You, Zhangyang Wang, Achuta Kadambi
First submitted to arxiv on: 10 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A text-to-3D 360° scene generation pipeline is proposed to create immersive virtual reality environments. The approach uses a 2D diffusion model and prompt self-refinement to generate high-quality panoramic images, which are then lifted into 3D Gaussians using splatting techniques for real-time exploration. To produce consistent 3D geometry, the pipeline constructs a spatially coherent structure by aligning monocular depth into a globally optimized point cloud. The method addresses invisible issues with single-view inputs by imposing semantic and geometric constraints on synthesized and input camera views as regularizations. This results in a globally consistent 3D scene within a 360° perspective, providing an enhanced immersive experience. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re exploring a virtual world! To make it feel super real, scientists created a new way to generate 3D scenes from text descriptions. They use a special computer model that can create panoramic images and lift them into 3D shapes for exploration. This helps fix problems with single-view inputs and creates a consistent scene that feels like you’re really there. |
Keywords
* Artificial intelligence * Diffusion model * Prompt
