  Predicting future human behavior from egocentric videos is a challenging but
critical task for human intention understanding. Existing methods for
forecasting 2D hand positions rely on visual representations and mainly focus
on hand-object interactions. In this paper, we investigate the hand forecasting
task and tackle two significant issues that persist in the existing methods:
(1) 2D hand positions in future frames are severely affected by ego-motions in
egocentric videos; (2) prediction based on visual information tends to overfit
to background or scene textures, posing a challenge for generalization on novel
scenes or human behaviors. To solve the aforementioned problems, we propose
EMAG, an ego-motion-aware and generalizable 2D hand forecasting method. In
response to the first problem, we propose a method that considers ego-motion,
represented by a sequence of homography matrices of two consecutive frames. We
further leverage modalities such as optical flow, trajectories of hands and
interacting objects, and ego-motions, thereby alleviating the second issue.
Extensive experiments on two large-scale egocentric video datasets, Ego4D and
EPIC-Kitchens 55, verify the effectiveness of the proposed method. In
particular, our model outperforms prior methods by $7.0$\% on cross-dataset
evaluations. Project page: https://masashi-hatano.github.io/EMAG/
