Neural Wireframe Renderer: Learning Wireframe to Image Translations

Xue, Yuan; Zhou, Zihan; Huang, Xiaolei

Abstract Recent advances in computer-aided design tools have helped rapidly advance the development of wireframe DNA origami nanostructures. Specifically, automated tools now exist that can convert an input polyhedral mesh into a DNA origami nanostructure, greatly reducing the design difficulty for wireframe DNA origami nanostructures. However, one limitation of these automated tools is that they require a designer to fully conceptualize their intended nanostructure, which may be limited by their own preconceptions. Here, a generative design framework is introduced capable of generating many wireframe DNA origami nanostructures without the need for a predefined mesh. User-defined objectives that guide the generative process are input as either single- or multi-objective optimization problems. A graph grammar is used to both contextualize physical properties of the DNA nanostructure and control the types of generated design features. This framework allows a designer to explore upon and ideate among many generated nanostructures that comply with their own unique constraints. A web-based graphical user interface is provided, allowing users to compare various generated solutions side by side in an interactive environment. Overall, this work illustrates how a constrained generative design framework can be implemented as an assistive tool in exploring design-feature trade-offs of wireframe DNA nanostructures, resulting in novel wireframe nanostructures.

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