MIT develops AI-powered tool for personalizing 3D-printable models

Adding custom design elements to 3D models can be a challenge for many makers. Complex and expensive computer-aided design (CAD) software is required, and it becomes particularly difficult if the original representation of the model is not available online. Additionally, ensuring that the customizations don't affect the functionality of the object requires expertise that many novice makers lack.

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To address these challenges, MIT researchers have developed a generative AI-driven tool called Style2Fab. This tool allows users to add custom design elements to 3D models using natural language prompts, without compromising their functionality. Style2Fab uses deep-learning algorithms to partition the model into aesthetic and functional segments, streamlining the design process.

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The researchers began the project by studying 3D models available in repositories to understand their functionalities. This understanding helped them develop AI algorithms to segment models into functional and aesthetic components. It was concluded that AI alone cannot accurately determine which parts of the model are functional, so human input and oversight are crucial in the design process.

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Style2Fab allows users to accept or modify the initial segmentation of the 3D model. Then, users can describe their desired design elements using natural language prompts, and an AI system called Text2Mesh generates a 3D model that meets their criteria. The system manipulates the aesthetic segments of the model while keeping the functional segments intact. The tool provides a user-friendly interface that automates segmentation and stylizing based on user inputs.

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The researchers conducted a study involving makers with varying levels of experience in 3D modeling. The study found that Style2Fab was particularly useful for novice users, providing a low barrier to entry and room for experimentation. Experienced users also benefited from the tool by speeding up their workflows and offering fine-grained control over stylizations.

In the future, the researchers aim to enhance Style2Fab by allowing users to control physical properties along with geometry. They also plan to enable users to generate their own custom 3D models from scratch within the system. The team is collaborating with Google on a follow-up project.

This research was supported by the MIT-Google Program for Computing Innovation and utilized the facilities provided by the MIT Center for Bits and Atoms.