Generative Creativity: Adversarial Learning for Bionic Design

Abstract

Generative creativity in the context of visual data refers to the generation process of new and creative images by composing features of existing ones. In this work, we aim to achieve generative creativity by learning to combine spatial features of images from different domains. We focus on bionic design as an ideal task for this study, in which a target object (e.g. a floor lamp) is designed to contain features of biological source objects (e.g. flowers), resulting in creative biologically-inspired design. Specifically, given an input image of a design target object, a generative model should learn to generate images that (1) maintain shape features of the input design target image, (2) contain shape features of images from the specified biological source domain, (3) are plausible and diverse. We propose DesignGAN, a novel unsupervised deep generative approach to realising shape-oriented bionic design. DesignGAN employs an adversarial learning architecture with designated losses to generate images that meet the three aforementioned requirements of bionic design modelling. We perform qualitative and quantitative experiments to evaluate our method, and demonstrate that our proposed framework successfully generates creative images of bionic design.