Perceiving Age Beyond Reality: Human and Machine Judgments of Virtual and AI-Generated Humans - Xavier, Ana Carolina de Oliveira; Araujo, Victor Flávio de Andrade; Knob, Paulo Ricardo; Musse, Soraia Raupp (Anais do Seminário Integrado de Software e Hardware (SEMISH), 2026)
This paper investigates how age is perceived and estimated in three categories of facial representations: Real Humans (photographs), Virtual Humans (3D MetaHuman renderings), and Digital Humans (photorealistic faces generated by a text-to-image model). We evaluate two age ranges (young: 20–30; older: 60–70) using a triangulated methodology: (i) four commercial age-estimation applications, (ii) a vision–language model (CLIP) under a zero-shot protocol, and (iii) an online user study (N = 112). Results indicate that commercial apps and CLIP exhibit their largest errors on 3D Virtual Humans (VHs), suggesting a domain generalization gap, while Artificial Intelligence (AI) generated 2D faces can be easier to classify than some real photographs. Human judgments show high accuracy for young AI-generated faces, but older AI-generated faces produce polarized responses. Overall, 3D VHs remain the most challenging domain for robust age perception.