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A better way to turn 2D designs into 3D models for rapid prototyping
A better way to turn 2D designs into 3D models for rapid prototyping
Researchers developed an automated framework that helps AI models generate CAD programs more accurately and efficiently.
Adam Zewe<br>MIT News
Publication Date:
July 16, 2026
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“GIFT” is a new system that teaches vision-language generative AI models to produce accurate, computer-aided design programs that can be used to simulate and test 3D objects.
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Credit: Christine Daniloff, MIT; iStock
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Caption:
“GIFT” is a new system that teaches vision-language generative AI models to produce accurate, computer-aided design programs that can be used to simulate and test 3D objects.
Credits:
Credit: Christine Daniloff, MIT; iStock
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Engineers often use vision-language models to produce new designs, such as for airplane or automobile components. To simulate how those components will perform in realistic situations, they’ll use tried-and-true computer-aided design (CAD) software to generate 3D models of those designs, which they can put through virtual crash or durability tests.<br>Researchers from MIT and elsewhere have now developed a system that can teach a vision-language model to automatically convert 2D designs into CAD programs that are much more accurate and functional compared to other approaches, while using only a fraction of the computation.<br>By improving the performance and efficiency of AI-driven CAD generation, this technique could streamline the rapid prototyping process and reduce costs. It could also help engineers identify beneficial design choices they might otherwise overlook.<br>The system generates new data based on the model’s abilities as it attempts to convert a 2D image into a CAD program. The framework corrects the model’s failures and incorporates them into a dataset with its successful solutions.<br>It uses these data to teach the model how to fix specific mistakes and tackle tricky problems it would struggle with on its own.<br>“We want engineers to be able to point our framework at an underperforming CAD model, set a compute budget, and let the system take over — turning the model’s own mistakes into better training data,” says lead author Giorgio Giannone, a research affiliate in the Design Computation and Digital Engineering (DeCoDE) Lab at MIT and a principal research scientist on the AI Innovation Team at Red Hat.<br>He is joined on the paper by Anna Claire Doris, a mechanical engineering graduate student at MIT; Amin Heyrani Nobari, an MIT postdoc; Kai Xu of RedHat; and co-senior authors Akash Srivastava, director of Core AI at IBM and a principal investigator at the MIT-IBM Computing Research Lab; and Faez Ahmed, associate professor of mechanical engineering at MIT, leader of the DeCoDE Lab, and a principal investigator at the MIT-IBM Computing Research Lab. The research was recently presented at the International Conference on Machine Learning.<br>“Nearly every physical product around us, from airplanes to appliances, begins its life as a CAD model. Industry teams are eager for AI that can help speed-up the creation of these designs, but today's models often produce simple shapes inadequate for practice. What excites me about this work is that it gives many image-to-CAD-code models a way...