GazeMotion: Gaze-guided Human Motion Forecasting
Zhiming Hu Syn Schmitt Daniel Haeufle Andreas Bulling
We present GazeMotion, a novel method for human motion forecasting that
combines information on past human poses with human eye gaze. Inspired by
evidence from behavioural sciences showing that human eye and body movements
are closely coordinated, GazeMotion first predicts future eye gaze from past
gaze, then fuses predicted future gaze and past poses into a gaze-pose graph,
and finally uses a residual graph convolutional network to forecast body
motion. We extensively evaluate our method on the MoGaze, ADT, and GIMO
benchmark datasets and show that it outperforms state-of-the-art methods by up
to 7.4% improvement in mean per joint position error. Using head direction as a
proxy to gaze, our method still achieves an average improvement of 5.5%. We
finally report an online user study showing that our method also outperforms
prior methods in terms of perceived realism. These results show the significant
information content available in eye gaze for human motion forecasting as well
as the effectiveness of our method in exploiting this information.