SaccadeNet: Towards Real-time Saccade Prediction for Virtual Reality Infinite Walking

Modern Redirected Walking (RDW) techniques significantly outperform classical solutions. Nevertheless, they are often limited by their heavy reliance on eye-tracking hardware embedded within the VR headset to reveal redirection opportunities. We propose a novel RDW technique that leverages the temporary blindness induced due to saccades for redirection. However, unlike the state-of-the-art, our approach does not impose additional eye-tracking hardware requirements. Instead, SaccadeNet, a deep neural network, is trained on head rotation data to predict saccades in real-time during an apparent head rotation. Rigid transformations are then applied to the virtual environment for redirection during the onset duration of these saccades. However, SaccadeNet is only effective when combined with moderate cognitive workload that elicits repeated head rotations. We present three user studies. The relationship between head and gaze directions is confirmed in the first user study, followed by the training data collection in our second user study. Then, after some fine-tuning experiments, the performance of our RDW technique is evaluated in a third user study. Finally, we present the results demonstrating the efficacy of our approach. It allowed users to walk up a straight virtual distance of at least 38 meters from within a $3.5 x 3.5m^2$ of the physical tracked space. Moreover, our system unlocks saccadic redirection on widely used consumer-grade hardware without eye-tracking.