DeepCloud. The Application of a Data-driven, Generative Model in Design

TL;DR

DeepCloud is a data-driven generative system that uses machine learning to create innovative design alternatives from existing solutions, offering an intuitive interface for designers.

Contribution

It introduces DeepCloud, a novel autoencoder-based generative system that learns design spaces from data without explicit specifications, enhancing creative design processes.

Findings

Two prototypes demonstrate DeepCloud's capabilities.

The system enables intuitive, data-driven design generation.

Potential to expand generative design applications.

Abstract

Generative systems have a significant potential to synthesize innovative design alternatives. Still, most of the common systems that have been adopted in design require the designer to explicitly define the specifications of the procedures and in some cases the design space. In contrast, a generative system could potentially learn both aspects through processing a database of existing solutions without the supervision of the designer. To explore this possibility, we review recent advancements of generative models in machine learning and current applications of learning techniques in design. Then, we describe the development of a data-driven generative system titled DeepCloud. It combines an autoencoder architecture for point clouds with a web-based interface and analog input devices to provide an intuitive experience for data-driven generation of design alternatives. We delineate the implementation of two prototypes of DeepCloud, their contributions, and potentials for generative design.