6 research outputs found
Towards Co-Creative Generative Adversarial Networks for Fashion Designers
Originating from the premise that Generative Adversarial Networks (GANs)
enrich creative processes rather than diluting them, we describe an ongoing PhD
project that proposes to study GANs in a co-creative context. By asking How can
GANs be applied in co-creation, and in doing so, how can they contribute to
fashion design processes? the project sets out to investigate co-creative GAN
applications and further develop them for the specific application area of
fashion design. We do so by drawing on the field of mixed-initiative
co-creation. Combined with the technical insight into GANs' functioning, we aim
to understand how their algorithmic properties translate into interactive
interfaces for co-creation and propose new interactions.Comment: Published at GenAICHI, CHI 2022 Worksho
Fashion Style Generation: Evolutionary Search with Gaussian Mixture Models in the Latent Space
This paper presents a novel approach for guiding a Generative Adversarial
Network trained on the FashionGen dataset to generate designs corresponding to
target fashion styles. Finding the latent vectors in the generator's latent
space that correspond to a style is approached as an evolutionary search
problem. A Gaussian mixture model is applied to identify fashion styles based
on the higher-layer representations of outfits in a clothing-specific attribute
prediction model. Over generations, a genetic algorithm optimizes a population
of designs to increase their probability of belonging to one of the Gaussian
mixture components or styles. Showing that the developed system can generate
images of maximum fitness visually resembling certain styles, our approach
provides a promising direction to guide the search for style-coherent designs.Comment: - to be published at: International Conference on Computational
Intelligence in Music, Sound, Art and Design : EvoMUSART 2022 - typo
corrected in abstrac