An Analysis of Face Synthesis Methods and Their Influence on Human Perception

Abstract

Synthetic faces (e.g., computer-generated characters) have been increasingly utilized across

various fields, including entertainment, healthcare, and education. Perceptual studies are often con- ducted to understand how synthetic faces are perceived by humans, aiming to enhance both quality

and user experience in these domains. Over the years, numerous methods have been developed to

create synthetic faces, ranging from traditional techniques such as image composites, Active Ap- pearance Models, and 3D Morphable Models to more recent machine-learning-based frameworks

like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). Despite the growing adoption of synthetic face generation and the variety of algorithms

available for their creation, cognitive scientists have underutilized these advanced techniques. Re- search in this area has remained largely dependent on earlier approaches, and even when Artificial- Intelligence-based (e.g., AI-based) methods are employed, there is an overreliance on GANs, particu- larly StyleGAN2. This trend highlights a significant gap in the exploration of alternative generative

architectures in perceptual studies. Furthermore, existing studies have primarily focused on the technical performance of GANs and VAEs, while human perception of their outputs has remained underexplored.

This dissertation seeks to address this gap by first providing a comprehensive review of syn- thetic face generation methods from a perceptual standpoint. Second, it analyzes and perceptually

compares two prominent AI-based models: StyleGAN2 (a GAN variant) and NVAE (a VAE flavor) across multiple contexts (e.g., full scenes vs. isolated faces without background, and animated vs. static faces) to determine how these conditions influence perceived realism and trustworthiness. This

comparison supports the development of cognitive research that advances the generation of percep- tually engaging and practically useful synthetic faces. Finally, conducting a study to investigate how

truthful versus misleading medical information about dementia influenced participants’ perceptions when viewing videos of synthetic faces generated by StyleGAN2. The outcomes of this dissertation provide insights into the perceptual differences between GAN-based and VAE-based synthetic faces across diverse contexts. Understanding these distinctions will contribute to the responsible and effective application of synthetic faces in real-life applications.