Leveraging Generative AI Models to Explore Human Identity

TL;DR

This paper uses diffusion-based generative AI models to investigate how external factors influence human identity, establishing a novel link between AI face generation and human identity formation, and creating an artwork to express this fluidity.

Contribution

It introduces a method to relate AI-generated face variations to human identity dynamics and presents a new concept called Fluidity of Human Identity through art.

Findings

External input changes lead to significant face variations in diffusion models

Confirmed dependence of human identity on external factors

Created a video artwork illustrating identity fluidity

Abstract

This paper attempts to explore human identity by utilizing neural networks in an indirect manner. For this exploration, we adopt diffusion models, state-of-the-art AI generative models trained to create human face images. By relating the generated human face to human identity, we establish a correspondence between the face image generation process of the diffusion model and the process of human identity formation. Through experiments with the diffusion model, we observe that changes in its external input result in significant changes in the generated face image. Based on the correspondence, we indirectly confirm the dependence of human identity on external factors in the process of human identity formation. Furthermore, we introduce \textit{Fluidity of Human Identity}, a video artwork that expresses the fluid nature of human identity affected by varying external factors. The video is available at https://www.behance.net/gallery/219958453/Fluidity-of-Human-Identity?.