Learning Geometry-Aware Representation for Gaze Estimation

Appearance-based gaze estimation has achieved remarkable progress in recent years. However, the inherent geometry characteristics of eye and facial areas are not fully explored in existing methods, which limits the generalization and robustness of the model. In this paper, we propose a novel end-to-end framework for cross-domain gaze estimation by integrating latent geometric representation into appearance-based gaze framework. More specifically, we first exploit the 3DMM method to fit unconstrained faces and eyes in the wild, which would generate adaptive normal information with explicit 3D geometry prior. Then we joint the normal map and the corresponding RGB appearance information to infer the 3D gaze direction with carefully-designed spatial-frequency attention and local-global feature interaction modules. The key to our method is to integrate explicit 3D geometry representation into a 2D learning architecture, which leads to a better trade-off between performance and efficiency. Experiments on both MPIIGaze and EyeDiap datasets demonstrate that the proposed method achieves the state-of-the-art accuracy of 3.56° and 5.10° separately, and also presents superior generalization ability on cross-domain dataset evaluations.