A Novel Framework for Multi-Person Temporal Gaze Following and Social
  Gaze Prediction
Anshul Gupta Samy Tafasca Arya Farkhondeh Pierre Vuillecard Jean-Marc Odobez
Gaze following and social gaze prediction are fundamental tasks providing
insights into human communication behaviors, intent, and social interactions.
Most previous approaches addressed these tasks separately, either by designing
highly specialized social gaze models that do not generalize to other social
gaze tasks or by considering social gaze inference as an ad-hoc post-processing
of the gaze following task. Furthermore, the vast majority of gaze following
approaches have proposed static models that can handle only one person at a
time, therefore failing to take advantage of social interactions and temporal
dynamics. In this paper, we address these limitations and introduce a novel
framework to jointly predict the gaze target and social gaze label for all
people in the scene. The framework comprises of: (i) a temporal,
transformer-based architecture that, in addition to image tokens, handles
person-specific tokens capturing the gaze information related to each
individual; (ii) a new dataset, VSGaze, that unifies annotation types across
multiple gaze following and social gaze datasets. We show that our model
trained on VSGaze can address all tasks jointly, and achieves state-of-the-art
results for multi-person gaze following and social gaze prediction.