  Gaze estimation, the task of predicting where an individual is looking, is a
critical task with direct applications in areas such as human-computer
interaction and virtual reality. Estimating the direction of looking in
unconstrained environments is difficult, due to the many factors that can
obscure the face and eye regions. In this work we propose CrossGaze, a strong
baseline for gaze estimation, that leverages recent developments in computer
vision architectures and attention-based modules. Unlike previous approaches,
our method does not require a specialised architecture, utilizing already
established models that we integrate in our architecture and adapt for the task
of 3D gaze estimation. This approach allows for seamless updates to the
architecture as any module can be replaced with more powerful feature
extractors. On the Gaze360 benchmark, our model surpasses several
state-of-the-art methods, achieving a mean angular error of 9.94 degrees. Our
proposed model serves as a strong foundation for future research and
development in gaze estimation, paving the way for practical and accurate gaze
prediction in real-world scenarios.
