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.

GazeMotion是一种结合过去人体姿势和人眼注视信息进行人体动作预测的新方法，通过预测未来眼睛注视点并将其与过去的姿势融合成注视-姿势图，再利用残差图卷积网络预测身体运动。在多个基准数据集的评估中，我们的方法在关节位置误差的平均改进上超过了最先进的方法7.4％。即使使用头部方向作为眼睛注视的替代，我们的方法的平均改进率也达到了5.5％。我们还通过在线用户研究展示了我们方法在感知逼真度方面超过之前方法的结果。这些结果展示了眼睛注视在人体动作预测中的重要信息内容以及我们方法在利用这些信息方面的有效性。

GazeMotion: 凝视引导的人体动作预测