Shaping Realities: Enhancing 3D Generative AI with Fabrication Constraints
arxiv(2024)
摘要
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.
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