A Tool to Facilitate the Cross-Cultural Design Process Using Deep Learning

Leijing Zhou, Xu Sun, Guannan Mu, Jiayi Wu, Jiangping Zhou, Qiuning Wu, Yaorun Zhang, Yufan Xi, N. D. Gunes, Siyang Song

Research output: Journal PublicationArticlepeer-review

Abstract

Cross-cultural design requires designers to understand other foreign cultures, selecting suitable cultural elements, and finally incorporate them into product design. Traditionally, this process is time-consuming and relies to a significant extent on designers' cultural awareness and design skills. This article proposes a new tool for designers to select and integrate cultural elements in the cross-cultural design process. The proposed approach utilizes state-of-the-art deep learning techniques, which begins by automatically selecting the most suitable style image from all cultural image candidates. Then, the deep-learning-based style transfer technique is introduced to automatically produce a design image that has the same content as the uploaded design content image, and also has the cultural style of the selected style image. To the best of our knowledge, this is the first work that extends deep learning techniques to facilitate cross-cultural design. The tool received positive feedback in a usability evaluation. The empirical results show that our approach can effectively increase designers' cultural awareness in respect of four cultural element dimensions (color, material, pattern and form). It is an innovative and efficient tool to help designers with idea generation and fast prototyping, although some participants argued that the tool would only assist designers, rather than replace humans.

Original languageEnglish
Pages (from-to)445-457
Number of pages13
JournalIEEE Transactions on Human-Machine Systems
Volume52
Issue number3
DOIs
Publication statusPublished - 1 Jun 2022

Keywords

  • Cross-cultural
  • deep learning
  • design tool

ASJC Scopus subject areas

  • Human Factors and Ergonomics
  • Control and Systems Engineering
  • Signal Processing
  • Human-Computer Interaction
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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