Synthetic generation of photorealistic 3D characters for face-based episodic memory measures

In Episodic Memory (EM) research, face identification and association tasks have emerged as sensitive indicators of memory decline. While 2D images have been widely used in face-based tasks, 3D representations offer unique advantages that can enhance ecological validity of stimuli, by incorporating dynamic behaviours that more closely resemble real-world conditions, such as variations in pose, facial expressions, and movements. Generative Adversarial Networks (GAN) can be a powerful tool for creating photorealistic facial stimuli for these experiments. Unlike traditional methods, GAN can allow researchers to systematically control a variety of facial features, while preserving each model's identity. This article introduces a novel approach to creating stimuli for EM and other face-based cognitive research, combining GAN-based photorealistic face generation with seamless 3D full-body character integration. This method provides parametric control over head pose and facial gesture, along with age, sex, racial attributes. Crucially, this approach introduces a systematic quantification and parametric control over the perceptual differences between facial identities, that bridges a critical methodological gap in face-based cognitive research. Results from a validation test with 313 volunteers provides compelling evidence for the efficacy of this method and the validity of the generated stimuli.