Summary of Progress and Prospects in 3d Generative Ai: a Technical Overview Including 3d Human, by Song Bai et al.
Progress and Prospects in 3D Generative AI: A Technical Overview including 3D human
by Song Bai, Jie Li
First submitted to arxiv on: 5 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Graphics (cs.GR)
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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 This research paper explores the rapidly growing field of 3D generation, which has seen significant advancements since 2023. The authors highlight the synergy between enhanced fidelity in stable diffusion, control methods ensuring multi-view consistency, and realistic human models like SMPL-X, leading to the creation of highly consistent and near-realistic 3D models. Neural network-based approaches such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have accelerated efficiency and realism in neural rendered models. The paper also discusses the multimodality capabilities of large language models, enabling language inputs to generate human motion outputs. This comprehensive overview summarizes relevant papers published during the latter half of 2023, covering AI-generated object models, 3D human models, and 3D human motions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research makes it possible for computers to create super-realistic 3D objects, people, and movements using special algorithms and a lot of data. It’s like having a magic pen that can draw anything you imagine! The scientists used new ways to make pictures look more real, and they even taught computers how to turn words into actions. This is really cool because it could help us create better movies, video games, and even robots that can move around like humans. |
Keywords
» Artificial intelligence » Diffusion » Neural network
