Virtual biotextile generation using generative adversarial networks: a study on bacterial cellulose for sustainable design

With increasing attention to sustainable material development, bio-based materials such as bacterial cellulose (BC) have been widely explored in design research. However, their broader application remains constrained by long production cycles and limited controllability. To address these limitations, this study presents an exploratory investigation of AI-based virtual biomaterial generation under limited-data conditions. Specifically, an image dataset was first constructed from self-cultivated BC samples produced under controlled settings. Three generative adversarial network (GAN)-based models were trained and evaluated for virtual material generation. User perception experiments revealed that StyleGAN-generated samples received the highest ratings in naturalness, sense of growth, and credibility, approaching evaluations of physical BC samples. In addition, an image-to-3D proof-of-concept showed that GAN-generated textures could be used as visual inputs for preliminary 3D shape generation, while cross-material experiments demonstrated the potential for simulating novel material combinations. The findings indicate that, despite limited training data, GAN-generated textiles can capture key visual characteristics of BC and facilitate early-stage design exploration. By integrating biological experimentation with computational generation, this work proposes a AI-assisted framework with the potential to reduce physical material consumption and support sustainable design innovation. The study further suggests that generative models can function not only as visualization tools but also as assistive systems for future material design.