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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Sep 4·5 min read
GazeFS: Target-Centered Gaze-Trajectory Forecasting and Stabilization from Gaze-Head History
TL;DR
GazeFS is a novel approach for predicting and stabilizing gaze trajectories based on historical gaze-head data, significantly reducing target acquisition errors. It achieves a reduction in bias and dispersion, enhancing target-centered gaze interaction.
✦ Why It Matters
Implement GazeFS to improve gaze interaction accuracy in your applications today.
Key Takeaways
- Utilize GazeFS for enhanced gaze trajectory predictions.
- Incorporate gaze-head history for improved target acquisition.
- Monitor bias and dispersion metrics for performance evaluation.
- Experiment with endpoint-free replay to maintain focus advantages.
How It Works
GazeFS leverages a variable-length history of gaze and head movements to predict the next target-centered gaze direction. It employs a forecasting model that stabilizes gaze trajectories by considering task-related dynamics, allowing for more accurate targeting without needing explicit target information during inference.