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 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/

Welcome to your official daily AI research tl;dr (often with code and news) for AI professionals where I share the most exciting papers I find daily, along with a one-liner summary to help you quickly determine if the article (and code) is worth investigating. This paper introduces the first approach to the reconstruction of high-resolution, high-dynamic range color images from raw photographic bursts captured by a handheld camera (phone) with exposure bracketing.