The success of investigative interviews with maltreated children is often defined by the interviewer’s ability to elicit a reliable and coherent account of the alleged incident from the child. Research shows that a child avatar mimicking a maltreated child can improve interviewers’ performance in conducting these interviews. The realism of such a child avatar is considered one of the most critical factors. Based on this, the current study aims to generate realistic child avatars in real-time that utilize multimodal data and different components from artificial intelligence. This paper discusses the subjective findings of a study of two types of child avatar videos; animated avatars created using the Unity game engine and photorealism talking-head avatars using Generative adversarial networks (GANs). The results show that although the state-of-the-art GAN-generated avatars are significantly more realistic, they do not necessarily create a better experience, as most of the participants prefer talking to animated avatars.
Is More Realistic Better? A Comparison of Game Engine and GAN-based Avatars for Investigative Interviews of Children
