Summary of Cad-prompted Generative Models: a Pathway to Feasible and Novel Engineering Designs, by Leah Chong et al.
CAD-Prompted Generative Models: A Pathway to Feasible and Novel Engineering Designs
by Leah Chong, Jude Rayan, Steven Dow, Ioanna Lykourentzou, Faez Ahmed
First submitted to arxiv on: 11 Jul 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 The paper introduces a method to improve the feasibility of design concepts generated by text-to-image models like Stable Diffusion 2.1 in engineering design tasks. By prompting the generation with feasible CAD images, the model produces more realistic and innovative bike designs when compared to traditional text prompts. The results show that setting the prompting weight around 0.35 achieves a balance between feasibility and novelty. This method has potential applications in various engineering design domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps engineers create better bike designs using AI. It uses a special kind of AI model called Stable Diffusion 2.1, which creates images based on text prompts. The researchers added CAD images to the prompt to make the generated designs more realistic and feasible. They tested this method with different settings and found that by adjusting the “prompting weight”, they can get a balance between creating new and innovative ideas while still keeping them practical. |
Keywords
» Artificial intelligence » Diffusion » Prompt » Prompting
