Abstract
Generative AI has greatly enhanced the production of digital content and has had a significant impact on the creative activities of designers. However, generalized generative AI falls short of designers' expectations in semantic understanding and image generation, and thus been poorly used in specific design domains (e.g., automotive design). This paper aims to explore the integration of generative AI into the design process, focusing on the generation of automotive design. We deconstructed the process of automotive design through user research, extracted the needs and pain points of designers, and transformed them into fine-tuning tasks for generative models. We trained three models in different styles based on Stable Diffusion: abstract forms, digital painting, and realistic rendering, and integrated them into the design workflow for practical automotive design. Performance evaluations and user studies indicate that our auxiliary models and generative design process can produce satisfactory automotive design and enhance efficiency.
Keywords
generative ai; automotive design; stable diffusion; human-ai collaboration
DOI
https://doi.org/10.21606/drs.2024.1260
Citation
Li, Y., Li, Y., Yan, W., Yang, F., and Ding, X. (2024) Advancing Design With Generative AI: A Case of Automotive Design Process Transformation, in Gray, C., Ciliotta Chehade, E., Hekkert, P., Forlano, L., Ciuccarelli, P., Lloyd, P. (eds.), DRS2024: Boston, 23–28 June, Boston, USA. https://doi.org/10.21606/drs.2024.1260
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Conference Track
Research Paper
Included in
Advancing Design With Generative AI: A Case of Automotive Design Process Transformation
Generative AI has greatly enhanced the production of digital content and has had a significant impact on the creative activities of designers. However, generalized generative AI falls short of designers' expectations in semantic understanding and image generation, and thus been poorly used in specific design domains (e.g., automotive design). This paper aims to explore the integration of generative AI into the design process, focusing on the generation of automotive design. We deconstructed the process of automotive design through user research, extracted the needs and pain points of designers, and transformed them into fine-tuning tasks for generative models. We trained three models in different styles based on Stable Diffusion: abstract forms, digital painting, and realistic rendering, and integrated them into the design workflow for practical automotive design. Performance evaluations and user studies indicate that our auxiliary models and generative design process can produce satisfactory automotive design and enhance efficiency.