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Despite the recent development of learning-based gaze estimation methods,most methods require one or more eye or face region crops as inputs and producea gaze direction vector as output. Cropping results in a higher resolution inthe eye regions and having fewer confounding factors (such as clothing andhair) is believed to benefit the final model performance. However, thiseye/face patch cropping process is expensive, erroneous, andimplementation-specific for different methods. In this paper, we propose aframe-to-gaze network that directly predicts both 3D gaze origin and 3D gazedirection from the raw frame out of the camera without any face or eyecropping. Our method demonstrates that direct gaze regression from the rawdownscaled frame, from FHD/HD to VGA/HVGA resolution, is possible despite thechallenges of having very few pixels in the eye region. The proposed methodachieves comparable results to state-of-the-art methods in Point-of-Gaze (PoG)estimation on three public gaze datasets: GazeCapture, MPIIFaceGaze, and EVE,and generalizes well to extreme camera view changes.

EFE: End-to-end Frame-to-Gaze Estimation