1 research outputs found
Personality-Driven Gaze Animation with Conditional Generative Adversarial Networks
We present a generative adversarial learning approach to synthesize gaze
behavior of a given personality. We train the model using an existing data set
that comprises eye-tracking data and personality traits of 42 participants
performing an everyday task. Given the values of Big-Five personality traits
(openness, conscientiousness, extroversion, agreeableness, and neuroticism),
our model generates time series data consisting of gaze target, blinking times,
and pupil dimensions. We use the generated data to synthesize the gaze motion
of virtual agents on a game engine.Comment: 7 pages, 5 figure