Modeling of Eye contact behavior

With the rise of online platforms and avatar-based communication, understanding eye contact a key non-verbal cue is crucial for trust in conversations. This study examines eye contact behavior across face-to-face interactions, a screen-sized window interface, and online meetings.

We collected twelve hours of eye contact data from 48 individuals using eye trackers and motion capture in dyadic settings. Our analysis showed consistent eye contact patterns in face-to-face and screen-sized window interactions, while online meetings caused significant shifts due to the lack of direct eye contact.

To model this behavior, we trained a diffusion model (DDPM) to generate synthetic eye movements that preserved key features of real data. We evaluated our model using metrics such as eye contact frequency. This study provides insights into how communication media influence gaze behavior and explores methods for generating realistic eye movements in conversational settings.