Summary of Graphic Design with Large Multimodal Model, by Yutao Cheng et al.
Graphic Design with Large Multimodal Model
by Yutao Cheng, Zhao Zhang, Maoke Yang, Hui Nie, Chunyuan Li, Xinglong Wu, Jie Shao
First submitted to arxiv on: 22 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 presents Hierarchical Layout Generation (HLG), a more flexible and pragmatic approach to automating graphic design. The existing Graphic Layout Generation (GLG) method is limited by the need for a predefined correct sequence of layers, restricting creative potential and increasing user workload. To overcome this constraint, the authors introduce Graphist, a layout generation model based on large multimodal models. Graphist reframes the HLG task as a sequence generation problem, using RGB-A images as input and outputting a JSON draft protocol indicating the coordinates, size, and order of each design element. The authors develop new evaluation metrics for HLG and demonstrate that Graphist outperforms prior arts, establishing a strong baseline for this field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make graphic design more accessible by creating a better way to arrange design elements. Right now, designers have to follow a specific order when arranging their designs, which limits their creativity and takes up too much of their time. The authors of this paper came up with a new method called Hierarchical Layout Generation (HLG) that makes it easier for designers to create beautiful and cohesive designs. |
