Generative Creativity: Adversarial Learning for Bionic Design

Yu, Simiao; Dong, Hao; Wang, Pan; Wu, Chao; Guo, Yike

doi:10.1007/978-3-030-30508-6_42

Cited by 8 publications

(6 citation statements)

References 37 publications

(64 reference statements)

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“…Since a well-trained network can continuously produce original content, creative support for design ideation using this technology is considered to have considerable potential. Yu et al (2019) propose DesignGAN, an unsupervised deep generation method for implementing shape-oriented bionic designs. It maintains the shape features of the input design target image and includes the shape features of images from a specified biological source domain.…”

Section: Generative Image-based Ideation Approachmentioning

confidence: 99%

Exploring the Impact of Generative Stimuli on the Creativity of Designers in Combinational Design

Han

2023

Proc. Des. Soc.

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The ideation process has a significant impact on the initial concept generation and final product creativity of the design. Visual stimuli play an important role in the process of innovative product design. With the increase in computing capability, generative design methods are widely implemented. In this paper, features of design targets and combinational objects in 2 combinational design tasks are fused using adversarial neural generative networks to form the generated stimuli. It is also used with combinational object pictures to investigate the impact on creativity in design ideation. The study invited designers to use and subjectively self-evaluate the two stimuli in a design task. Through analysis of participant data (n=20), the results showed that the generative stimuli had an advantage over the combinational image stimuli in terms of the smoothness of creativity in the design ideation of outcomes. And there is a positive correlation between designers' years of design education and their tendency to prefer generative stimuli. Based on the results obtained, ideas are provided for the study of the influence of visual and generative stimuli on the designer's ideation process.

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Section: Generative Image-based Ideation Approachmentioning

confidence: 99%

Exploring the Impact of Generative Stimuli on the Creativity of Designers in Combinational Design

Han

2023

Proc. Des. Soc.

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“…Computational methods such as Generative Adversarial Network (Goodfellow et al, 2014) are similar to these approaches, which can generate new sketches that borrow features from a sort of object in particular styles. For instance, DesignGAN is an approach for generating a shape-oriented bionic design that combines the features from a biological source domain and the shape of a design target (Yu et al, 2018). In this case, the human designer is indispensable for the design process to gain desirable results with preference from clients.…”

Section: Computational Creativitymentioning

confidence: 99%

A Computational Approach to Generate Design With Specific Style

Zhang

et al. 2021

Proc. Des. Soc.

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Creativity is crucial in design. In recent years, growing computational methods are applied to improve the creativity of design. This paper aims to explore an approach to generate creative design images with specific feature or design style. A Generative Adversarial Network model is applied in the approach to learn the specific design style. The target products will be projected into the latent space of model to transfer their styles and generate images. The generated images combine the features of the specific design style and the features of the target product. In the experiment, the approach using the generated images to inspire the human designer to generate the creative design in according styles. According to the primary verification by participants, the generated images can bring novelty and surprise to participants, which gain the positive impact on human creativity.

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“…In a more high-level exploration, researchers have started to apply deep learning in auto design generation. Yu et al (2018) proposed DesignGAN to generate a shape-oriented bionic design that maintains the shape of the design target and combines the features from the biological source domain. Also inspired by bionic design, Duncan et al (2015) presented a method for generating zoomorphic shapes by merging a man-made shape and an animal shape.…”

Section: Deep Learning For Designmentioning

confidence: 99%

“…For example, image style transfer (Efros & Freeman 2001; Dosovitskiy & Brox 2016; Gatys et al 2016; Isola et al 2017 a ) can be used to generate an image with the original content but different style features. Generative bionics design (Yu et al 2018) employs an adversarial learning approach to generate images containing both features from the design target and biological source. However, these artificial intelligence (AI) image generation methods do not consider human aspects, which means the results are generated in variations but lack human cognition input.…”

Section: Introductionmentioning

confidence: 99%

Neurocognition-inspired design with machine learning

Wang

Peng

et al. 2020

Des. Sci.

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Generating designs via machine learning has been an on-going challenge in computer-aided design. Recently, deep learning methods have been applied to randomly generate images in fashion, furniture and product design. However, such deep generative methods usually require a large number of training images and human aspects are not taken into account in the design process. In this work, we seek a way to involve human cognitive factors through brain activity indicated by electroencephalographic measurements (EEG) in the generative process. We propose a neuroscience-inspired design with a machine learning method where EEG is used to capture preferred design features. Such signals are used as a condition in generative adversarial networks (GAN). First, we employ a recurrent neural network Long Short-Term Memory as an encoder to extract EEG features from raw EEG signals; this data are recorded from subjects viewing several categories of images from ImageNet. Second, we train a GAN model conditioned on the encoded EEG features to generate design images. Third, we use the model to generate design images from a subject’s EEG measured brain activity. To verify our proposed generative design method, we present a case study, in which the subjects imagine the products they prefer, and the corresponding EEG signals are recorded and reconstructed by our model for evaluation. The results indicate that a generated product image with preference EEG signals gains more preference than those generated without EEG signals. Overall, we propose a neuroscience-inspired artificial intelligence design method for generating a design taking into account human preference. The method could help improve communication between designers and clients where clients might not be able to express design requests clearly.

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Generative Creativity: Adversarial Learning for Bionic Design

Cited by 8 publications

References 37 publications

Exploring the Impact of Generative Stimuli on the Creativity of Designers in Combinational Design

Exploring the Impact of Generative Stimuli on the Creativity of Designers in Combinational Design

A Computational Approach to Generate Design With Specific Style

Neurocognition-inspired design with machine learning

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