When is Fake Real Enough? Evaluating StyleGAN2 and NVAE Face Realism Across Animation and Visual Contexts

As generative AI models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are increasingly used to synthesize faces for different kinds of applications, understanding how humans perceive these synthetic faces is critical. Prior work mainly focused on the technical differences between GANs and VAEs, while users' perceived realism of these generated faces remained unexplored. This paper investigates perceptual differences by comparing a GAN‐based model (e.g., StyleGAN2) and a VAE‐based model (e.g., NVAE). We evaluate how the perceived realism of images generated by these two AI models is influenced by different contexts: entire scenes, isolated faces without backgrounds, and both static and animated formats. Our findings revealed significant context‐dependent differences in perceived realism, highlighting how model type and presentation format influence visual authenticity. These insights are particularly relevant for applications in conversational user interfaces, where generated faces could serve as avatars or virtual agents. Understanding perceptual realism can help designers create more engaging human‐AI interactions.