Huang, J;
Chen, B;
Yan, Z;
Ounis, I;
Wang, J;
(2023)
GEO: A Computational Design Framework for Automotive Exterior Facelift.
ACM Transactions on Knowledge Discovery from Data
, 17
(6)
, Article 82. 10.1145/3578521.
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Abstract
Exterior facelift has become an effective method for automakers to boost the consumers' interest in an existing car model before it is redesigned. To support the automotive facelift design process, this study develops a novel computational framework - Generator, Evaluator, Optimiser (GEO), which comprises three components: a StyleGAN2-based design generator that creates different facelift designs; a convolutional neural network (CNN)-based evaluator that assesses designs from the aesthetics perspective; and a recurrent neural network (RNN)-based decision optimiser that selects designs to maximise the predicted profit of the targeted car model over time. We validate the GEO framework in experiments with real-world datasets and describe some resulting managerial implications for automotive facelift. Our study makes both methodological and application contributions. First, the generator's mapping network and projection methods are carefully tailored to facelift where only minor changes are performed without affecting the family signature of the automobile brands. Second, two evaluation metrics are proposed to assess the generated designs. Third, profit maximisation is taken into account in the design selection. From a high-level perspective, our study contributes to the recent use of machine learning and data mining in marketing and design studies. To the best of our knowledge, this is the first study that uses deep generative models for automotive regional design upgrading and that provides an end-to-end decision-support solution for automakers and designers.
| Type: | Article |
|---|---|
| Title: | GEO: A Computational Design Framework for Automotive Exterior Facelift |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3578521 |
| Publisher version: | https://doi.org/10.1145/3578521 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10169737 |
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