Summary of A Comprehensive Survey on 3d Content Generation, by Jian Liu et al.
A Comprehensive Survey on 3D Content Generation
by Jian Liu, Xiaoshui Huang, Tianyu Huang, Lu Chen, Yuenan Hou, Shixiang Tang, Ziwei Liu, Wanli Ouyang, Wangmeng Zuo, Junjun Jiang, Xianming Liu
First submitted to arxiv on: 2 Feb 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 a comprehensive review of 3D content generation, a rapidly advancing field in artificial intelligence-generated content (AIGC). The authors propose a new taxonomy for existing approaches, categorizing them into three types: 3D native generative methods, 2D prior-based 3D generative methods, and hybrid 3D generative methods. The survey covers approximately 60 papers spanning major techniques in the field. The authors discuss limitations of current 3D content generation techniques and point out open challenges and promising directions for future work. The review also includes a project website providing resources on 3D content generation research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper reviews how computers can create 3D images and objects, like what you see in movies or video games. It’s an important area of study because it helps us understand how to make computers smarter. The authors group different methods for creating 3D things into three categories. They also look at the strengths and weaknesses of these methods and suggest new areas to explore. |
