MechStyle: Augmenting Generative AI with Mechanical Simulation to Create Stylized and Structurally Viable 3D Models
Name
3745778.3766655.pdf
Size
8.16 MB
Format
Adobe PDF
Checksum (MD5)
316c1db3bf6bb2c8d8ba07243a326915
Author(s) • • • • • • • • •
Faruqi, Faraz
Abdel-Rahman, Amira
Tejedor, Leandra
Nisser, Martin
Li, Jiaji
Phadnis, Vrushank
Jampani, Varun
Gershenfeld, Neil
Hofmann, Megan
Mueller, Stefanie
Date Issued
November 19, 2025
Publisher
ACM|ACM Symposium on Computational Fabrication
Citation
Faraz Faruqi, Amira Abdel-Rahman, Leandra Tejedor, Martin Nisser, Jiaji Li, Vrushank Phadnis, Varun Jampani, Neil Gershenfeld, Megan Hofmann, and Stefanie Mueller. 2025. MechStyle: Augmenting Generative AI with Mechanical Simulation to Create Stylized and Structurally Viable 3D Models. In Proceedings of the ACM Symposium on Computational Fabrication (SCF '25). Association for Computing Machinery, New York, NY, USA, Article 24, 1–15.
Version
Final published version
Abstract
Recent developments in Generative AI enable creators to stylize 3D models based on text prompts. These methods change the 3D model geometry, which can compromise the model’s structural integrity once fabricated. We present MechStyle, a system that enables creators to stylize 3D printable models while preserving their structural integrity. MechStyle accomplishes this by augmenting the Generative AI-based stylization process with feedback from a Finite Element Analysis (FEA) simulation. As the stylization process modifies the geometry to approximate the desired style, feedback from the FEA simulation reduces modifications to regions with increased stress. We evaluate the effectiveness of FEA simulation feedback in the augmented stylization process by comparing three stylization control strategies. We also investigate the time efficiency of our approach by comparing three adaptive scheduling strategies. Finally, we demonstrate MechStyle’s user interface that allows users to generate stylized and structurally viable 3D models and provide five example applications.
Description
SCF ’25, Cambridge, MA, USA
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Center for Bits and Atoms
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3745778.3766655