MIT researchers have developed a tool called Style2Fab that allows users to customize 3D models without compromising their functionality. Many novice makers struggle to add personalized design elements to 3D models because it requires expensive CAD software and expertise. Style2Fab solves this problem by using deep-learning algorithms to automatically divide the model into aesthetic and functional segments. Users can then use natural language prompts to describe their desired design, and Style2Fab will stylize the model accordingly. The tool is especially useful in the field of DIY assistive technology, where patients and clinicians may not have the expertise to personalize 3D-printable medical models. Style2Fab also offers fine-grained control over stylizations, making it useful for both novice and experienced users. In the future, the researchers aim to enhance the tool by allowing users to control physical properties and create custom 3D models from scratch. The research was supported by the MIT-Google Program for Computing Innovation.

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