Shaping Realities: Enhancing 3D Generative AI with Fabrication Constraints

TL;DR

This paper discusses enhancing 3D generative AI by integrating fabrication constraints to produce models that are both aesthetically appealing and physically viable for real-world manufacturing.

Contribution

It proposes augmentations to existing 3D generative AI tools to incorporate physical and structural constraints, bridging digital creativity with physical fabrication requirements.

Findings

Identifies limitations of current AI tools in physical model generation

Proposes methods to include physical constraints in 3D generative processes

Highlights potential for more functional 3D models from AI-generated designs

Abstract

Generative AI tools are becoming more prevalent in 3D modeling, enabling users to manipulate or create new models with text or images as inputs. This makes it easier for users to rapidly customize and iterate on their 3D designs and explore new creative ideas. These methods focus on the aesthetic quality of the 3D models, refining them to look similar to the prompts provided by the user. However, when creating 3D models intended for fabrication, designers need to trade-off the aesthetic qualities of a 3D model with their intended physical properties. To be functional post-fabrication, 3D models have to satisfy structural constraints informed by physical principles. Currently, such requirements are not enforced by generative AI tools. This leads to the development of aesthetically appealing, but potentially non-functional 3D geometry, that would be hard to fabricate and use in the real world. This workshop paper highlights the limitations of generative AI tools in translating digital creations into the physical world and proposes new augmentations to generative AI tools for creating physically viable 3D models. We advocate for the development of tools that manipulate or generate 3D models by considering not only the aesthetic appearance but also using physical properties as constraints. This exploration seeks to bridge the gap between digital creativity and real-world applicability, extending the creative potential of generative AI into the tangible domain.