Summary of The Scene Language: Representing Scenes with Programs, Words, and Embeddings, by Yunzhi Zhang et al.
The Scene Language: Representing Scenes with Programs, Words, and Embeddings
by Yunzhi Zhang, Zizhang Li, Matt Zhou, Shangzhe Wu, Jiajun Wu
First submitted to arxiv on: 22 Oct 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 proposed Scene Language is a novel visual representation that efficiently captures the structure, semantics, and identity of visual scenes. It consists of three components: a program specifying hierarchical and relational entity structures, natural language words summarizing semantic classes, and visual embeddings capturing each entity’s appearance. This representation can be inferred from pre-trained language models without additional training, given text or image inputs. The resulting scene can be rendered into high-quality images using various graphics renderers. Compared to existing scene graphs, the Scene Language generates complex scenes with higher fidelity while providing precise control and editing capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a way to describe a picture or a video in simple words, like “a car driving on a road” or “a person standing in a park.” The Scene Language is a new method that can do just that. It’s like a recipe for describing the scene: what’s happening, who’s there, and what everything looks like. This recipe can be used to create realistic images of the scene from scratch, using words or pictures as input. The Scene Language makes it easier to generate high-quality 3D and 4D scenes, with more details and control. |
Keywords
» Artificial intelligence » Semantics
