Summary of L3go: Language Agents with Chain-of-3d-thoughts For Generating Unconventional Objects, by Yutaro Yamada et al.
L3GO: Language Agents with Chain-of-3D-Thoughts for Generating Unconventional Objects
by Yutaro Yamada, Khyathi Chandu, Yuchen Lin, Jack Hessel, Ilker Yildirim, Yejin Choi
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 paper proposes a language agent called L3GO that can reason about physical and spatial configurations of objects, unlike current data-driven diffusion models. The agent uses large language models as agents to compose desired objects via trial-and-error within a 3D simulation environment. A new benchmark, Unconventionally Feasible Objects (UFO), is developed, along with SimpleBlenv, a wrapper environment built on top of Blender where language agents can build and compose atomic building blocks. Human and automatic evaluations show that the approach surpasses standard GPT-4 and other language agents for 3D mesh generation on ShapeNet. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you want to describe an object with unusual features, like “a chair with five legs.” Current AI models struggle to create such objects. This paper introduces a new way of thinking called L3GO that can reason about the physical world and generate 3D objects based on text descriptions. It’s like using words to build something in a virtual world. |
Keywords
* Artificial intelligence * Diffusion * Gpt
