D.M.A.I.H.: Deepfake-Inspired Few-Shot Learning Approach with Stable Diffusion for Digital Mourning

Digital mourning (deuil numérique) is the use of digital and AI-based technologies to preserve, recontextualize, and extend the memory of deceased loved ones through personalized and meaningful virtual representations. The digital mourning process requires innovative technologies capable of preserving the memory of deceased loved ones in meaningful and humanized ways. This paper proposes a novel generative approach, D.M.A.I.H. (Digital Mourning with Artificial Intelligence for Healing), for digital grief, with a focus on moral support and the mental health of bereaved relatives, using Stable Diffusion with a few-shot learning adaptation mechanism. The system takes as input a small set of personal references (e.g. a portrait, contextual images such as the person’s home, and a short descriptive script) and outputs high-quality, photorealistic images of the deceased in different meaningful contexts, process closely related to deepfake generation but redirected here toward therapeutic and commemorative purposes. Unlike traditional generative models requiring large datasets, Few-shot personalization is leveraged to adapt Stable Diffusion to each individual with minimal data, enabling the generation of personalized digital albums. Experimental results show that the model consistently preserves identity in the images it produces, and contextual control ensures emotional resonance. In particular, identity similarity scores for the generated images ranged from 0.88 to 0.93, with an average score of 0.91, testifying to strong identity preservation across all outputs. The innovation of this study is a foundation for AI-based memorialization, balancing technological innovation with concerns over privacy, authenticity and cultural sensitivity, and psychological comfort.