  Bionic design refers to an approach of generative creativity 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. In this work, we attempt to model the process of shape-oriented bionic
design as follows: given an input image of a design target object, the model
generates 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 bionic design. Specifically,
we employ a conditional Generative Adversarial Networks architecture with
several designated losses (an adversarial loss, a regression loss, a cycle loss
and a latent loss) that respectively constrict our model to meet the
corresponding aforementioned requirements of bionic design modelling. We
perform qualitative and quantitative experiments to evaluate our method, and
demonstrate that our proposed approach successfully generates creative images
of bionic design.
