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Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

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Daily Overview

Session

PAPERS: Human-AI Design Collaboration
Location: Appleton Tower, LT2
Zoom Link
Accessibility: https://www.accessable.co.uk/the-university-of-edinburgh/central-area/access-guides/appleton-lecture-theatre-2-edinburgh
Session Chair: Beatriz Itzel Cruz Megchun, University of Portland
Session Chair: Michael Stead, Imagination Design Research Lab, School of Arts, Lancaster University, United Kingdom

Presentation 3

AI-enhanced Chinese style furniture design: Integrating CNN cultural recognition with latent diffusion generation

Yupeng Jiang^1,2,3, Jiayong Zheng⁵, Bingchen Gou^1,2,3, Jie Song^1,4, Jianing Kang^1,2,3, Zhaoxian Ren^1,2,3, Ying Liu^1,2,3, Lingwei Lyu^1,2,3, Junyan Lin^1,2,3, Suyao Wei^1,2,3, Weilan An^1,2,3

1: School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, 710072, China; 2: Key Laboratory of Ministry of Industrial Design and Ergonomics, Ministry of Industry and Information Technology, Northwestern Polytechnical University, Xi’an 710072, China; 3: Shaanxi Engineering Laboratory for Industrial Design, Northwestern Polytechnical University, Xi’an 710072, China; 4: Sustainable Building and Environmental Research Institute, Northwestern Polytechnical University, Xi’an, 710129, China;; 5: Hubei University of Technology, Wuhan, 430068, China

In the wave of artificial intelligence development, employing computer technology to advance the innovative design of Chinese-style furniture and optimize the intelligent development of cultural products is crucial for revitalizing the industry and strengthening cultural confidence. This study identifies issues of homogenization and cultural dilution in intelligent furniture design and proposes an intelligent design model based on convolutional neural networks (CNNs) and a latent diffusion model (LDM). CNNs are used to recognize cultural features in Chinese-style furniture, while the LDM generates design schemes, rapidly producing Chinese chairs that meet contemporary aesthetic preferences while retaining traditional cultural attributes. The study enhances the intelligent recognition and application of fine cultural details, avoids weakened aesthetic value caused by single-algorithm approaches, improves the efficiency of intelligent design for Chinese-style furniture, and offers new perspectives for the creative development of cultural products.

View Paper: https://doi.org/10.21606/drs.2026.964