Summary of 3d-scenedreamer: Text-driven 3d-consistent Scene Generation, by Frank Zhang et al.
3D-SceneDreamer: Text-Driven 3D-Consistent Scene Generation
by Frank Zhang, Yibo Zhang, Quan Zheng, Rui Ma, Wei Hua, Hujun Bao, Weiwei Xu, Changqing Zou
First submitted to arxiv on: 14 Mar 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 The paper presents an advancement in text-driven 3D scene generation techniques by introducing a novel refinement network. Current approaches rely on existing generative models, leading to error accumulation and limited applications. The proposed method employs a NeRF-based representation to constrain global 3D consistency and refines local views by aggregating global information. This approach leverages the natural image prior from 2D diffusion models and global 3D scene information. Experimental results show that the model supports various scenarios, including outdoor and unreal environments, with improved visual quality and 3D consistency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating realistic 3D scenes using text descriptions. Current methods are not very good because they rely on other models to generate images, which can lead to mistakes and limited uses. The new approach fixes this by refining the generated images using information from the entire scene. It’s like taking a picture of a whole city instead of just a small part. This makes it possible to create 3D scenes for many different scenarios, such as outdoor or fantasy environments. The results are more realistic and accurate. |
Keywords
» Artificial intelligence » Diffusion
