Summary of Graph Canvas For Controllable 3d Scene Generation, by Libin Liu and Shen Chen and Sen Jia and Jingzhe Shi and Zhongyu Jiang and Can Jin and Wu Zongkai and Jenq-neng Hwang and Lei Li
Graph Canvas for Controllable 3D Scene Generation
by Libin Liu, Shen Chen, Sen Jia, Jingzhe Shi, Zhongyu Jiang, Can Jin, Wu Zongkai, Jenq-Neng Hwang, Lei Li
First submitted to arxiv on: 27 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 paper introduces GraphCanvas3D, a novel framework for controllable 3D scene generation. The authors leverage in-context learning to enable dynamic adaptability without retraining, supporting flexible and customizable scene creation. The framework employs hierarchical, graph-driven scene descriptions, representing spatial elements as graph nodes and establishing coherent relationships among objects in 3D environments. Unlike conventional approaches, GraphCanvas3D allows for seamless object manipulation and scene adjustments on the fly, with experimental results demonstrating enhanced usability, flexibility, and adaptability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper is about creating 3D scenes that can change dynamically as new objects are added or removed. This is important because many AI systems need to understand and interact with physical environments in three dimensions. The authors introduce a new framework called GraphCanvas3D that makes it easy to create these dynamic scenes without having to retrain the system each time. They show that this approach is more flexible and easier to use than current methods. |
