Enhancing the Aesthetic Appeal of AI-Generated Physical Product Designs through LoRA Fine-Tuning with Human Feedback

TL;DR

This paper demonstrates that LoRA fine-tuning guided by human aesthetic feedback can significantly improve AI-generated physical product designs, enhancing their desirability and aesthetic appeal, with practical applications in 3D printing.

Contribution

It introduces a novel approach of integrating human aesthetic feedback into LoRA fine-tuning of AI models for tangible product design enhancement.

Findings

LoRA fine-tuning aligns AI designs with human aesthetic preferences.

Significant improvements in desirability and aesthetic scores of generated designs.

Effective methods for converting AI designs into tangible 3D printed products.

Abstract

This study explores how Low-Rank Adaptation (LoRA) fine-tuning, guided by human aesthetic evaluations, can enhance the outputs of generative AI models in tangible product design, using lamp design as a case study. By integrating human feedback into the AI model, we aim to improve both the desirability and aesthetic appeal of the generated designs. Comprehensive experiments were conducted, starting with prompt optimization techniques and focusing on LoRA fine-tuning of the Stable Diffusion model. Additionally, methods to convert AI-generated designs into tangible products through 3D realization using 3D printing technologies were investigated. The results indicate that LoRA fine-tuning effectively aligns AI-generated designs with human aesthetic preferences, leading to significant improvements in desirability and aesthetic appeal scores. These findings highlight the potential of human-AI collaboration in tangible product design and provide valuable insights into integrating human feedback into AI design processes.