  Appearance-based gaze estimation has been actively studied in recent years.
However, its generalization performance for unseen head poses is still a
significant limitation for existing methods. This work proposes a generalizable
multi-view gaze estimation task and a cross-view feature fusion method to
address this issue. In addition to paired images, our method takes the relative
rotation matrix between two cameras as additional input. The proposed network
learns to extract rotatable feature representation by using relative rotation
as a constraint and adaptively fuses the rotatable features via stacked fusion
modules. This simple yet efficient approach significantly improves
generalization performance under unseen head poses without significantly
increasing computational cost. The model can be trained with random
combinations of cameras without fixing the positioning and can generalize to
unseen camera pairs during inference. Through experiments using multiple
datasets, we demonstrate the advantage of the proposed method over baseline
methods, including state-of-the-art domain generalization approaches. The code
will be available at https://github.com/ut-vision/Rot-MVGaze.
