GazeMixup: Direction-Magnitude Aware Feature Interpolation for Cross-Domain Gaze Estimation

Appearance-based gaze estimation often suffers from poor cross-domain generalization due to variations in identity, illumination, imaging devices, and other domain-specific factors. We introduce GazeMixup, a geometry-aware feature-level augmentation method for domain-generalized gaze estimation. The key idea is to decompose gaze features into direction and magnitude components, motivated by the observed association between feature magnitude and prediction error. GazeMixup separately interpolates these components with magnitude-aware coefficients and applies the same interpolation principle to gaze labels, enabling adaptive feature synthesis while maintaining feature–label consistency. The method is parameter-free, requires no target-domain data, and can be seamlessly integrated into existing gaze estimation networks. Extensive experiments across multiple cross-domain tasks and backbone architectures show that GazeMixup consistently outperforms vanilla Mixup and improves the generalization of baseline gaze estimators on unseen domains, demonstrating the effectiveness of geometry-aware feature interpolation for cross-domain gaze estimation.