2025/03/25 by Paraskevi Valergaki, Valergaki, Paraskevi, Antonis Argyros +5 · 1 voice
Computer Science · Engineering · #Convolutional neural network #FOS: Computer and information sciences #FOS: Electrical engineering #Face (sociological concept) #Face recognition and analysis #Facial motion capture #Facial recognition system #Feature (linguistics) #Generative grammar #Graphics (cs.GR) #Image and Video Processing (eess.IV) #Perception #Representation (politics) #Virtual reality #cs.GR #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2503.20819
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/03/25 · arxiv published 2025/03/25 · arxiv updated 2025/05/01 · openalex created_date 2025/10/11 · openalex updated_date 2026/08/05
Real-time 3D face manipulation has significant applications in virtual reality, social media and human-computer interaction. This paper introduces a novel system, which we call Mirror of Diversity (MOD), that combines Generative Adversarial Networks (GANs) for texture manipulation and 3D Morphable Models (3DMMs) for facial geometry to achieve realistic face transformations that reflect various demographic characteristics, emphasizing the beauty of diversity and the universality of human features. As participants sit in front of a computer monitor with a camera positioned above, their facial characteristics are captured in real time and can further alter their digital face reconstruction with transformations reflecting different demographic characteristics, such as gender and ethnicity (e.g., a person from Africa, Asia, Europe). Another feature of our system, which we call Collective Face, generates an averaged face representation from multiple participants' facial data. A comprehensive evaluation protocol is implemented to assess the realism and demographic accuracy of the transformations. Qualitative feedback is gathered through participant questionnaires, which include comparisons of MOD transformations with similar filters on platforms like Snapchat and TikTok. Additionally, quantitative analysis is conducted using a pretrained Convolutional Neural Network that predicts gender and ethnicity, to validate the accuracy of demographic transformations.