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By borrowing the wisdom of human in gaze following, we propose a two-stagesolution for gaze point prediction of the target persons in a scene.Specifically, in the first stage, both head image and its position are fed intoa gaze direction pathway to predict the gaze direction, and then multi-scalegaze direction fields are generated to characterize the distribution of gazepoints without considering the scene contents. In the second stage, themulti-scale gaze direction fields are concatenated with the image contents andfed into a heatmap pathway for heatmap regression. There are two merits for ourtwo-stage solution based gaze following: i) our solution mimics the behavior ofhuman in gaze following, therefore it is more psychological plausible; ii)besides using heatmap to supervise the output of our network, we can alsoleverage gaze direction to facilitate the training of gaze direction pathway,therefore our network can be more robustly trained. Considering that existinggaze following dataset is annotated by the third-view persons, we build a videogaze following dataset, where the ground truth is annotated by the observers inthe videos. Therefore it is more reliable. The evaluation with such a datasetreflects the capacity of different methods in real scenarios better. Extensiveexperiments on both datasets show that our method significantly outperformsexisting methods, which validates the effectiveness of our solution for gazefollowing. Our dataset and codes are released inhttps://github.com/svip-lab/GazeFollowing.

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