  Gaze is a powerful form of non-verbal communication and social interaction
that humans develop from an early age. As such, modeling this behavior is an
important task that can benefit a broad set of application domains ranging from
robotics to sociology. In particular, Gaze Following is defined as the
prediction of the pixel-wise 2D location where a person in the image is
looking. Prior efforts in this direction have focused primarily on CNN-based
architectures to perform the task. In this paper, we introduce a novel
transformer-based architecture for 2D gaze prediction. We experiment with 2
variants: the first one retains the same task formulation of predicting a gaze
heatmap for one person at a time, while the second one casts the problem as a
2D point regression and allows us to perform multi-person gaze prediction with
a single forward pass. This new architecture achieves state-of-the-art results
on the GazeFollow and VideoAttentionTarget datasets. The code for this paper
will be made publicly available.
