Diversity in Virtual Humans: Unveiling Biases in Human Characteristics Representation - Araujo, Victor Flávio de Andrade; Pelachaud, Catherine; Costa, Angelo B.; Musse, Soraia R. (Anais Estendidos da Conference on Graphics, Patterns and Images (SIBGRAPI), 2024)
Recent advancements in Computer Graphics (CG) have significantly enhanced the realism of animations and characters in various media. However, the Uncanny Valley (UV) theory suggests that as Virtual Humans (VHs) become more realistic, they may evoke discomfort. This phenomenon challenges industry professionals and researchers to study human perception, considering diverse characteristics such as gender and skin color. This work investigates human perception and sensations when playing or watching VHs, aiming to answer many questions regarding their visual characteristics. For example, one question examines human perception concerning the relationship between the character’s gender and the participant’s gender. The results showed in-group advantages for participants regarding VHs with binary genders, both in gender attribution and emotion recognition. Additionally, this work explores solutions for deconstructing the gender binary using a genderless Virtual Baby (VB) and an adult VH model. It also discusses the UV effect on VHs with different skin colors, highlighting potential biases in skin color algorithms.