Is More Realistic Better? A Comparison of Game Engine and GAN-based Avatars for Investigative Interviews of Children.
International Conference on Multimedia Retrieval (ICMR)(2022)
摘要
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.
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