The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
A Generative AI Framework for Enhancing Human-AI Collaborative Creation in Artistic Design Services
Abstract
Amid information saturation and aesthetic pluralism, artistic design services grapple with inefficient manual workflows and imbalanced creative diversity-semantic fidelity. To address these and advance information system integration in design, this study proposes a two-stage multi-task generative AI framework for artistic design, integrating latent space remapping, hierarchical cross-modal attention distillation, and dynamic resource scheduling. Evaluated on a 30,000-sample dataset, the framework outperforms baselines: 45% lower FID than GAN-based models, 15% higher CLIP-Score for text-image alignment, over 4.3/5 professional designer satisfaction, and 1.2 iterations/second inference on a single 3080Ti GPU. It resolves existing generative AI flaws and advances human-AI collaboration in design services, laying technical groundwork for workflow innovation, design education support, and brand development.
Related Content
|
Nalinee Sophatsathit.
© 2026.
14 pages.
|
|
Min Jiang.
© 2026.
16 pages.
|
|
Samar El Sayad, Ahmed Diab, Mohamed Fawzy Elsayed, Laila Aladwey.
© 2026.
31 pages.
|
|
Zhengdong Hou.
© 2026.
13 pages.
|
|
Gevorg Harutyunyan, Karen Nersisyan, Lilit Galstyan, Lilik Beglaryan, Mikayel Mikayelyan, Grigor Manukyan.
© 2026.
20 pages.
|
|
Azadeh Amoozegar, Ali Nouri Lata, Mohammad Falahat, Sara Ravan Ramzani, Sedigheh Shakib, Mohamadreza Jafary, Mohd Hanafi Mohd Yasin.
© 2026.
21 pages.
|
|
Jingmiao Liu, Xiaoshuang Hou.
© 2026.
22 pages.
|
|
|