  Incorporating style-related objectives into shape design has been centrally
important to maximize product appeal. However, stylistic features such as
aesthetics and semantic attributes are hard to codify even for experts. As
such, algorithmic style capture and reuse have not fully benefited from
automated data-driven methodologies due to the challenging nature of design
describability. This paper proposes an AI-driven method to fully automate the
discovery of brand-related features. Our approach introduces BIGNet, a two-tier
Brand Identification Graph Neural Network (GNN) to classify and analyze scalar
vector graphics (SVG). First, to tackle the scarcity of vectorized product
images, this research proposes two data acquisition workflows: parametric
modeling from small curve-based datasets, and vectorization from large
pixel-based datasets. Secondly, this study constructs a novel hierarchical GNN
architecture to learn from both SVG's curve-level and chunk-level parameters.
In the first case study, BIGNet not only classifies phone brands but also
captures brand-related features across multiple scales, such as the location of
the lens, the height-width ratio, and the screen-frame gap, as confirmed by AI
evaluation. In the second study, this paper showcases the generalizability of
BIGNet learning from a vectorized car image dataset and validates the
consistency and robustness of its predictions given four scenarios. The results
match the difference commonly observed in luxury vs. economy brands in the
automobile market. Finally, this paper also visualizes the activation maps
generated from a convolutional neural network and shows BIGNet's advantage of
being a more human-friendly, explainable, and explicit style-capturing agent.
Code and dataset can be found on Github:
  1. Phone case study: github.com/parksandrecfan/bignet-phone 2. Car case
study: github.com/parksandrecfan/bignet-car
