Generative Solid Modelling Employing Natural Language Understanding and 3D Data

Abstract

The paper describes an experimental system for generating 3D-printable models inspired by arbitrary textual input. Utilizing a transliteration pipeline, the system pivots on Natural Language Understanding technologies and 3D data available via online repositories to result in a bag of retrieved 3D models that are then concatenated in order to produce original designs. Such artefacts celebrate a post-digital kind of objecthood, as they are concretely physical while, at the same time, incorporate the cybernetic encodings of their own making. Twelve individuals were asked to reflect on some of the 3D-printed, physical artefacts. Their responses suggest that the created artefacts succeed in triggering imagination, and in accelerating moods and narratives of various sorts.