Exploration of contemporary fashion design through StyleGAN, utilizing curated datasets of birds and dresses for generative modeling
2021
Generative AI DesignFashion TechnologyComputational CreativityMachine Learning
Tools | Google Collab, Adobe Illustrator, InDesign, Premiere Pro
Language | Python
I. CONCEPT
OVERVIEW
BirDress is an exploratory project that uses StyleGAN to draw visual
parallels between organic avian forms and contemporary haute couture. It
investigates how machine learning can translate structural and textural
cues from nature into fashion-adjacent imagery.
MOTIVATION
The project asks how organic life forms might inform design language.
While an ecological focus on extinct species was considered, limited
imagery shifted the emphasis toward visual experimentation and dataset
curation.
APPROACH
Training was performed with curated bird and fashion image sets. To
stabilize outputs, datasets were preprocessed for axial alignment so
aspect ratios and orientations corresponded before StyleGAN training.
II. DATA
DOMAINS
DATASETS
Bird imagery: Caltech-UCSD Birds 200 (CUB-200-2011) refined
to 83 curated images across visually distinctive species.
Fashion imagery: 400 images collected from Iris van Herpen’s
haute couture collections for their kinetic, organic qualities.
MODEL TRAINING
Early runs with single-domain data under-performed. Preprocessing both
datasets to align orientation and crop yielded more coherent hybrid
outputs, indicating visual structure and alignment can matter more than
dataset size at short training horizons.
III. RESULTS
6 HOURS
9 HOURS
IV. POSTERS
Created in the style of, and as an homage to, designer David Carson.
VI. INSIGHTS
TECHNICAL
Visual structure and axial alignment impacted short-horizon training more
than raw dataset size.
CONCEPTUAL
Intentional curation enables StyleGAN to generate compelling abstractions
that merge fashion and nature imagery.