Gaze-LLE: Gaze Target Estimation via Large-Scale Learned Encoders
Authors: name: Fiona Ryan, name: Ajay Bati, name: Sangmin Lee, name: Daniel Bolya, name: Judy Hoffman, name: James M. Rehg

We address the problem of gaze target estimation, which aims to predict where
a person is looking in a scene. Predicting a person's gaze target requires
reasoning both about the person's appearance and the contents of the scene.
Prior works have developed increasingly complex, hand-crafted pipelines for
gaze target estimation that carefully fuse features from separate scene
encoders, head encoders, and auxiliary models for signals like depth and pose.
Motivated by the success of general-purpose feature extractors on a variety of
visual tasks, we propose Gaze-LLE, a novel transformer framework that
streamlines gaze target estimation by leveraging features from a frozen DINOv2
encoder. We extract a single feature representation for the scene, and apply a
person-specific positional prompt to decode gaze with a lightweight module. We
demonstrate state-of-the-art performance across several gaze benchmarks and
provide extensive analysis to validate our design choices. Our code is
available at: http://github.com/fkryan/gazelle .