Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Yanru Lyu
 -- 1999 2023-12-28 04:27:04 |
2 format correct
Catherine Yang
 Meta information modification 1999 2023-12-28 04:34:55 |

Video Upload Options

Do you have a full video?
Send video materials Upload full video

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Lyu, Y.; Shi, M.; Zhang, Y.; Lin, R. Generative AI on Cultural Translation in Jewelry Design. Encyclopedia. Available online: https://encyclopedia.pub/entry/53201 (accessed on 03 May 2024).
Lyu Y, Shi M, Zhang Y, Lin R. Generative AI on Cultural Translation in Jewelry Design. Encyclopedia. Available at: https://encyclopedia.pub/entry/53201. Accessed May 03, 2024.
Lyu, Yanru, Minghong Shi, Yanbo Zhang, Rungtai Lin. "Generative AI on Cultural Translation in Jewelry Design" Encyclopedia, https://encyclopedia.pub/entry/53201 (accessed May 03, 2024).
Lyu, Y., Shi, M., Zhang, Y., & Lin, R. (2023, December 28). Generative AI on Cultural Translation in Jewelry Design. In Encyclopedia. https://encyclopedia.pub/entry/53201
Lyu, Yanru, et al. "Generative AI on Cultural Translation in Jewelry Design." Encyclopedia. Web. 28 December, 2023.
Generative AI on Cultural Translation in Jewelry Design
Edit
This entry is adapted from the peer-reviewed paper 10.3390/su16010065

The current proliferation of artificial intelligence (AI) is prominently shaping the design industry. Generative AI, such as text-to-image and image-to-image models, has gained widespread use, notably for its efficiency and quality improvements. Leveraging AI image generators has shifted the designer’s focus from technical tasks to strategic decisions related to visual appeal, cognitive engagement, and emotional resonance. Furthermore, the challenges inherent in human–AI collaboration have been revealed, stemming from communication difficulties and the risk of fixating on specific details to stylistic constraints.

generative AI imagination cultural translation jewelry design

1. Introduction

The process of design is characterized by its intricate and elusive nature, but creativity is manifested in the whole process, especially the early stages of design. Ideation in design, alternatively, creative idea generation, always plays a vital role in the early stages of production design. With the accelerating development of generative artificial intelligence (AI) algorithms in recent years, its application has achieved extraordinary outcomes in different fields, including the creative design [1]. To date, designers can harness and interact with various text-to-image or image-to-image Al models (e.g., Midjourney [2], Stable Diffusion [3], OpenAI’s DALL-E 2 [4], and Deep Dream Generator [5]), which could facilitate in augmenting creativity and stimulating visual imagination and ideation [6][7].
Jewelry, being a highly personalized and swiftly iterative product category, necessitates a multitude of creative concepts to flourish, thus rendering it a pivotal domain suitable for the implementation of AI. It is essential to recognize that this specific product category transcends mere adornment and extends to encapsulate emotions and attitudes [8]. As Hong Kong jewelry designer Dickson Yewn stated, jewelry is “the new way to interpret a culture” [9]. Social progress and cultural development have an obvious influence on the forms, functions, and meanings of jewelry designs [10]. Cultural sustainability indicates the capacity to maintain and perpetuate cultural beliefs, practices, and heritage, while also ensuring the continuation of culture in the future [11]. Within the realm of design, cultural value can be embedded in products through creative design, which involves integrating cultural elements into new artistic forms to pass on multiple cultures to consumers and activate the cultural value [12][13]. However, the potential of human–AI collaboration in setting cultural translation as a design goal has not yet received wide attention.

2. Computer-Aided Design

Technology is of great importance in the design process, providing designers with a series of computer-aided design (CAD) tools, including graphic software, 3D modeling software, and digital rendering platforms. The CAD for concept generation can be categorized into three types according to their potential roles in the human–computer collaborative relationship: guide, stimulation, and generation [14]. The methods to guide are instructively involved in the generation of design concepts, which aim to provide design rules or guidelines for the activities of the designers, such as team-based brainstorming and mind mapping. Stimulation refers to knowledge-based methods and visual stimuli like images and sketches, which can influence design performance [15]. Generation denotes that designers can use various software to draw sketches, pictures, or three-dimensional prototypes, which is a crucial step in shaping creativity and can help designers further communicate and evaluate ideas [16].
With the rapid advancement in generative AI technology in recent years, more intelligent new design tools and functions are emerging, which then integrate a series of generative AI models such as generative pre-trained transformers (GPTs), generative deep models (GDMs), and generative adversarial networks (GANs) [17][18]. These have received increasing interest from the design community, including the field of jewelry design. Generative AI tools represented by Midjourney [2] and Stable Diffusion [3] can help designers quickly complete designs with high accuracy and repeatability. They use prompts as inputs combined with image generation algorithms with large language models to facilitate text-guided image generation. The basic logic is to combine or merge a large number of ideas to produce much stronger ones [6].
Additionally, increasing studies concentrate on the challenges and opportunities of human–AI collaboration in diverse contexts [19][20][21]. Based on some empirical studies, the AI agent assists designers in diversifying their solutions to fulfill the design objectives and presents great potential to guide more explorative design strategies [22][23]. However, in terms of generative AI, there is a great limitation that they are currently only capable of learning and generating spatially visualized representations of design concepts based on training data sets, which are mainly helpful for detail design and may cause design fixation if applied in earlier stages [16].
Although CAD has been widely used in jewelry design to improve efficiency, the current system focuses more on quick modeling (e.g., Rhinoceros 3D, Blender, ZBrush). There were few tools to assist in generating conceptual design before the emergence of generative AI. An earlier AI-design practice named “the chair project”, took generative adversarial neural networks (GANs) as automatisms to stimulate the imagination of human designers [24]. Limited by the washout problem of GANs, generative images still need designers to handle most visualization tasks and to add details based on design principles and creativity. The latest applications of AI are to generate visual images from natural language descriptions and initial image guides, which can create thousands of higher-quality images with minimal effort [6].
The use of new technology forces designers to broaden their understanding of the design process and adapt their methods, ultimately leading to the potential generation of innovative products [25][26]. A novel form of production is based on past models but only comes into being in response to individual demand. Therefore, the advantages and design limitations brought by AI technology make human–computer interaction more complex. While there is methodological readiness for designing with CAD, the nuances of human–AI collaboration need to be further explored from the perspective of the workflow in generative AI-augmented design creativity (Figure 1). The mapping from potential ideas to specific elements is closely associated with the experience and knowledge of the creator [21]. After the translation from inputted elements such as images or text description, the AI model handles the next stage to decoding, latent diffusion, and encoding in the pixel space to generate numerous new related images. Both training data sets and parameter settings influence the outputs. Moreover, it may bring positive value, such as guidance, stimulation, and generation, while also having an impact on design fixation.
Figure 1. The workflow in generative AI-augmented design creativity.

3. Activities in Human–AI Collaboration

In Cross’s earlier studies, he found that seemingly, the only ”positive” effect of CAD was to speed up the design process, while reducing the person-power required in the team [27]. However, he believed human–machine interaction would efficiently produce design solutions better than that which a human or a machine could produce alone. To find the answer to whether the machine can design, it is necessary to analyze the design activities of people [28]. Unlike conventional liner technology that achieves one outcome based on one action, AI poses challenges to the creative process and workflow, characterized by its learning pattern and random generation [29]. Computer design research informs us concerning designers’ thinking [28]. Facing infinite and limitless sources of information, requirements, wants and needs, limitations, and opportunities, the designer has to make all kinds of decisions and judgments [30]. Numerous studies on design practice have indicated that designers who can handle complex design situations successfully use design thinking and activities [31][32]. The utilization of emerging technologies stimulates designers to expand their understanding of the design process and alter their design methods, which in turn has potentially generated new products [26]. Moreover, incorporating AI into products, services, and systems is complex, requiring the attention of the design discipline, and the initial answers might be hidden in the design cognition [33].
The design process involves a continuous series of decision-making activities that collectively shape a final outcome [34]. Design as research is an approach that combines the principles of design and research methodologies to address complex problems and generate new knowledge. It involves using design processes and methods to investigate, explore, and understand problems, as well as to propose and test potential solutions [35][36]. Designing is intimately tied to new technologies that generate a complex set of decision points for designers. The changes, problems, and solutions of traditional design influenced by technology can be explored from the design process [26][37]. In collaboration with generative AI tools, the activities, such as what prompts should be inputs and which images can be chosen to further update, are usually driven by decisions. Then, decision-making is also influenced by experience, knowledge, and preferences. Visual stimulation can assist the perception and interpretation of more ephemeral phenomena by color, texture, form, images, and status [38]. Iteration is considered an integral part of design activity that may be classified as the diagnostic and transformative process [39][40]. Adams and Atman empirically explored the iteration behavior in the design process, recognizing that compared to beginners in design, senior designers iterate more times and more effectively [41]. Focusing on the impact of AI on design iteration, Zhou et al. conducted a graphic design experiment and results indicate AI augmentation increases the frequency of cognitive activities during iteration [23]. Actually, the activities of human–AI collaboration encompass a complex cyclical process, as shown in Figure 2.
Figure 2. The workflow of generative AI-aided design creativity.

4. Cultural Translation in Jewelry Design

Currently, more attention is given to the role and importance of culture in sustainable development. Cultural features are considered unique features that can be embedded in products to achieve sustainable cultural spread and value transformation [42][43]. Therefore, it is crucial to confirm the methods for integrating cultural elements into creative product design. Based on previous research, the factors that affect cultural and creative product design include aesthetics, creativity, practicality, and commercial value, which are taken into consideration [44][45]. Lee [46] introduced the term “cultural variable”, which means to profile cultural characteristics and identify the key design attributes, such as functional, aesthetic, and symbolic ones. There, cultural objects can be incorporated into cultural design. Lin [43] identified three design features: inner-level (e.g., stories, emotions, and cultural features), mid-level (e.g., function, operational concerns, usability, and safety), and outer-level (e.g., color, texture, form, decoration, surface pattern, line quality, and detail). To bridge the implicit layer of culture and an explicit layer of design characteristics, a series of culture-embedded product design models were proposed and developed from the perspective of communication [43][47][48], which divided the design and perceptual process of the product into the technical level, semantic level, and effectiveness level. The technical level requires attracting the users through cultural features through their senses; the semantic level focuses on precisely transmitting cultural information; and the effectiveness level concerns the emotional connection and reflection.
Traditional jewelry design is time-consuming and labor-intensive. It requires a high level of drafting skills. Furthermore, the unique jewelry design can be created by harnessing different pattern combinations in varying proportions, but it leads to a very tedious design process [24]. While several designers have embraced traditional Chinese elements such as dragons, the phoenix, bamboo, and Chinese characters into their products, they occasionally do not apply or combine them properly, making them become less meaningful [9]. On the contrary, appropriate combinations of elements can accurately convey cultural information. Designers pass the new concept into the jewelry through the material, color, craft technique, etc., which causes the wearer’s spiritual resonance [49]. From the coding perspective of jewelry design, the external features of design include shape, craftsmanship, and materials. The combination of these features also carries cultural metaphors, ultimately conveying a sense of ritual and pleasure [9][50], as displayed in Figure 3.
Figure 3. The research framework for the impact of generative AI on cultural translation in jewelry design.
The development of technology could indeed have an impact on the actions of design creativity, both positively and negatively. Especially in the collaborative creation process between humans and generative AI technology, the path in design thinking shows new features. Culture and creative designing are, after all, a form of designing. The discussion about the impact of generative AI on the ideation process must be firmly based on the model of conventional design. Therefore, this article is concerned with the impact of generative AI technology on the ideation of embedded culture features in jewelry design and explores the positive and negative effects of technology on design thinking from a cultural perspective.

References

  1. Hwang, A.H.-C. Too late to be creative? AI-empowered tools in creative processes. In Proceedings of the CHI Conference on Human Factors in Computing Systems Extended Abstracts, New Orleans, LA, USA, 29 April 2022; pp. 1–9.
  2. Midjourney. Available online: www.midjourney.com (accessed on 25 April 2023).
  3. Rombach, R.; Blattmann, A.; Lorenz, D.; Esser, P.; Ommer, B. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA, 19–24 June 2022; pp. 10684–10695.
  4. Ramesh, A.; Dhariwal, P.; Nichol, A.; Chu, C.; Chen, M. Hierarchical text-conditional image generation with clip latents. arXiv 2022, arXiv:2204.06125.
  5. Deep Dream Generator. Available online: https://deepdreamgenerator.com/ (accessed on 25 April 2023).
  6. Eapen, T.T.; Finkenstadt, D.J.; Folk, J.; Venkataswamy, L. How Generative AI Can Augment Human Creativity. Harv. Bus. Rev. 2023, 101, 55–64.
  7. Liao, Q.V.; Subramonyam, H.; Wang, J.; Wortman Vaughan, J. Designerly understanding: Information needs for model transparency to support design ideation for AI-powered user experience. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, Hamburg, Germany, 23 April 2023; pp. 1–21.
  8. Yu, Q.; Meng, K.; Guo, J. Research on innovative application of silver material in modern jewelry design. MATEC Web Conf. 2018, 176, 02013.
  9. Lucas, A.; Chapin, M.; Lin, M.; Jia, X. The Chinese Soul in Contemporary Jewelry Design. Gems Gemol. 2015, 51, 18–30.
  10. Yuan, X. Principle of Design to Follow Nature’s Course. Take Jewelry Design as an Example. In Proceedings of the 3rd International Conference on Arts, Design and Contemporary Education (ICADCE 2017), Moscow, Russia, 30 May 2017; pp. 504–507.
  11. Macagnan, C.B.; Seibert, R.M. Culture: A Pillar of Organizational Sustainability. In Ecotheology-Sustainability and Religions of the World; IntechOpen: London, UK, 2022.
  12. Zhao, C. Strategies for promoting the value of tourist cultural and creative brands based on cultural elements. E3S Web Conf. 2021, 251, 03028.
  13. Moalosi, R.; Setlhatlhanyo, K.N.; Sealetsa, O.J. Cultural Memory, an Asset for Design-Driven Innovation within the Creative Industries Sector: Lessons for Design Education. Des. Technol. Educ. 2016, 21, 9–22.
  14. Zhu, Q.; Luo, J. Generative transformers for design concept generation. J. Comput. Inf. Sci. Eng. 2023, 23, 041003.
  15. Goldschmidt, G.; Smolkov, M. Variances in the impact of visual stimuli on design problem solving performance. Des. Stud. 2006, 27, 549–569.
  16. Viswanathan, V.; Tomko, M.; Linsey, J. A study on the effects of example familiarity and modality on design fixation. AI EDAM 2016, 30, 171–184.
  17. Jovanovic, M.; Campbell, M. Generative artificial intelligence: Trends and prospects. Computer 2022, 55, 107–112.
  18. Muller, M.; Chilton, L.B.; Kantosalo, A.; Martin, C.P.; Walsh, G. GenAICHI: Generative AI and HCI. In Proceedings of the CHI Conference on Human Factors in Computing Systems Extended Abstracts, New Orleans, LA, USA, 30 April 2022; pp. 1–7.
  19. Quan, S.J.; Park, J.; Economou, A.; Lee, S. Artificial intelligence-aided design: Smart design for sustainable city development. Environ. Plan. B Urban Anal. City Sci. 2019, 46, 1581–1599.
  20. Hunde, B.R.; Woldeyohannes, A.D. Future prospects of computer-aided design (CAD)–A review from the perspective of artificial intelligence (AI), extended reality, and 3D printing. Results Eng. 2022, 14, 100478.
  21. Lyu, Y.; Wang, X.; Lin, R.; Wu, J. Communication in human–AI co-creation: Perceptual analysis of paintings generated by text-to-image system. Appl. Sci. 2022, 12, 11312.
  22. Song, B.; Soria Zurita, N.F.; Nolte, H.; Singh, H.; Cagan, J.; McComb, C. When faced with increasing complexity: The effectiveness of artificial intelligence assistance for drone design. J. Mech. Des. 2022, 144, 021701.
  23. Zhou, C.; Li, Y.; Wu, C.; Shao, Y.; Zhou, Y. Study of cognitive activity in AI-augmented design iteration support. In Proceedings of the 2021 14th International Symposium on Computational Intelligence and Design (ISCID), Hangzhou, China, 11 December 2020; pp. 78–82.
  24. Fatma, N.; Haleem, A.; Bahl, S.; Javaid, M. Prospects of jewelry designing and production by additive manufacturing. In Proceedings of the Current Advances in Mechanical Engineering: Select Proceedings of ICRAMERD 2020, Bhubaneswar, India, 24 July 2020; pp. 869–879.
  25. Katz, B.M. Review essay: Technology and design—A new agenda. Technol. Cult. 1997, 38, 452–466.
  26. Parsons, J.L.; Campbell, J. Digital apparel design process: Placing a new technology into a framework for the creative design process. Cloth. Text. Res. J. 2004, 22, 88–98.
  27. Cross, N. Human and Machine Roles in Computer-Aided Design; University of Manchester Institute of Science and Technology: Manchester, UK, 1974.
  28. Cross, N. Can a machine design? Des. Issues 2001, 17, 44–50.
  29. Audry, S. Art in the Age of Machine Learning; Mit Press: Cambridge, MA, USA, 2021.
  30. Stolterman, E. The nature of design practice and implications for interaction design research. Int. J. Des. 2008, 2, 55–65.
  31. Cross, N. Designerly Ways of Knowing: Design Discipline Versus Design Science. Des. Issues 2001, 17, 49–55.
  32. Moggridge, B.; Atkinson, B. Designing Interactions; MIT Press Cambridge: Cambridge, MA, USA, 2007; Volume 17.
  33. Stoimenova, N.; Price, R. Exploring the nuances of designing (with/for) artificial intelligence. Des. Issues 2020, 36, 45–55.
  34. Lee, J.H.; Ostwald, M.J. Creative decision-making processes in parametric design. Buildings 2020, 10, 242.
  35. Koskinen, I.; Zimmerman, J.; Binder, T.; Redstrom, J.; Wensveen, S. Design research through practice: From the lab, field, and showroom. IEEE Trans. Prof. Commun. 2013, 56, 262–263.
  36. Chi, L. Introduction: Design as Research. J. Archit. Educ. 2001, 54, 250.
  37. Pasricha, A.; Greeninger, R. Exploration of 3D printing to create zero-waste sustainable fashion notions and jewelry. Fash. Text. 2018, 5, 1–18.
  38. Garner, S.; McDonagh-Philp, D. Problem interpretation and resolution via visual stimuli: The use of ‘mood boards’ in design education. J. Art Des. Educ. 2001, 20, 57–64.
  39. Adams, R.S.; Atman, C.J. Cognitive processes in iterative design behavior. In Proceedings of the FIE’99 Frontiers in Education, 29th Annual Frontiers in Education Conference. Designing the Future of Science and Engineering Education, San Juan, PR, USA, 13 November 1999; Volume 11, pp. 11A16/13–11A16/18.
  40. Hybs, I.; Gero, J.S. An evolutionary process model of design. Des. Stud. 1992, 13, 273–290.
  41. Adams, R.S. Cognitive Processes in Iterative Design Behavior; University of Washington: Seattle, WA, USA, 2001.
  42. Hartley, J. Communication, Cultural and Media Studies: The Key Concepts; Routledge: Abingdon, UK, 2019.
  43. Lin, R.-T. Transforming Taiwan aboriginal cultural features into modern product design: A case study of a cross-cultural product design model. Int. J. Des. 2007, 1, 45–53.
  44. Hsueh, S.-L.; Zhou, B.; Chen, Y.-L.; Yan, M.-R. Supporting technology-enabled design education and practices by DFuzzy decision model: Applications of cultural and creative product design. Int. J. Technol. Des. Educ. 2022, 32, 2239–2256.
  45. Sun, X.; Jin, W.; Li, C. Research on the design of Nanjing museum cultural and creative product from the perspective of experience. In Proceedings of the Design, User Experience, and Usability: Understanding Users and Contexts: 6th International Conference, DUXU 2017, Held as Part of HCI International 2017, Vancouver, BC, Canada, 9–14 July 2017; Part III 6. pp. 529–539.
  46. Lee, K.-P. Design Methods for Cross-Cultural Collaborative Design Project. In Proceedings of the Futureground—DRS International Conference 2004, Melbourne, Australia, 17 November 2004.
  47. Wu, J.; Ju, L.-H.; Lin, P.-H.; Lyu, Y. The Relationship between Form and Ritual in Cultural Sustainability. Sustainability 2022, 14, 9157.
  48. Hsu, C.-H.; Lin, C.-L.; Lin, R. A study of framework and process development for cultural product design. In Proceedings of the Internationalization, Design and Global Development: 4th International Conference, IDGD 2011, Held as Part of HCI International 2011, Orlando, FL, USA, 9–14 July 2011; pp. 55–64.
  49. Gong, B.-H.; Yuan, R. Study of Contemporary Jewelry Design Emotional Expression Skills. J. Arts Humanit. 2017, 6, 57–61.
  50. Shi, M.; Zhang, C.; Ting, Y.; Lin, P.-H. Application of Auspicious Cultural in Metalworking Jewelry Design. In Proceedings of the Cross-Cultural Design, Applications in Health, Learning, Communication, and Creativity: 12th International Conference, CCD 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, 19–24 July 2020; pp. 548–558.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Cultural Studies
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Yanru Lyu
,
Minghong Shi
,
Yanbo Zhang
,
Rungtai Lin
View Times: 210
Revisions: 2 times (View History)
Update Date: 28 Dec 2023
Table of Contents
    1000/1000
    Hot Most Recent
    Feedback