As 3D printers have become cheaper and more widely accessible, a rapidly growing community of novice makers are fabricating their own objects. To do this, many of these amateur artisans access free, open-source repositories of user-generated 3D models that they download and fabricate on their 3D printer.
But adding custom design elements to these models poses a steep challenge for many makers, since it requires the use of complex and expensive computer-aided design (CAD) software, and is especially difficult if the original representation of the model is not available online. Plus, even if a user is able to add personalized elements to an object, ensuring those customizations don’t hurt the object’s functionality requires an additional level of domain expertise that many novice makers lack.
To help makers overcome these challenges, MIT researchers developed a generative-AI-driven tool that enables the user to add custom design elements to 3D models without compromising the functionality of the fabricated objects. A designer could utilize this tool, called Style2Fab, to personalize 3D models of objects using only natural language prompts to describe their desired design. The user could then fabricate the objects with a 3D printer.
then tries to figure out what a 3D model would look like that meets the user’s criteria.
It manipulates the aesthetic segments of the model in Style2Fab, adding texture and color or adjusting shape, to make it look as similar as possible. But the functional segments are off-limits.
The researchers wrapped all these elements into the back-end of a user interface that automatically segments and then stylizes a model based on a few clicks and inputs from the user.
They conducted a study with makers who had a wide variety of experience levels with 3D modeling and found that Style2Fab was useful in different ways based on a maker’s expertise. Novice users were able to understand and use the interface to stylize designs, but it also provided a fertile ground for experimentation with a low barrier to entry.
For experienced users, Style2Fab helped quicken their workflows. Also, using some of its advanced options gave them more fine-grained control over stylizations.
Moving forward, Faruqi and his collaborators want to extend Style2Fab so the system offers fine-grained control over physical properties as well as geometry. For instance, altering the shape of an object may change how much force it can bear, which could cause it to fail when fabricated. In addition, they want to enhance Style2Fab so a user could generate their own custom 3D models from scratch within the system. The researchers are also collaborating with Google on a follow-up project.
IMAGE CREDIT: Faraz Faruqi and Stefanie Mueller.
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