How Much Future Helps? A Controlled Study of Future-Privileged Supervision for Causal Egocentric Gaze Estimation

Egocentric gaze estimation is critical for augmented reality and assistive systems, yet many existing models rely on offline access to future video frames, which is incompatible with real-time, causal inference. This paper investigates whether future context provides valuable signals for gaze estimation and, if so, how much future look-ahead is optimal for training a strictly causal model. The authors introduce a controlled framework that uses a future-aware branch during training to provide privileged supervision, which is then discarded at inference. By keeping the visual encoder frozen and the inference architecture fixed, the study isolates the impact of future context from other model variables.

The authors find that future-privileged supervision consistently improves the performance of strictly causal gaze models across two major benchmarks, EGTEA Gaze+ and Ego4D. Crucially, the benefits of future context do not scale monotonically with longer look-ahead. Instead, performance peaks within a specific temporal regime: approximately 1.7 to 3.3 seconds (H=5 to 10) on EGTEA Gaze+ and 2.7 seconds (H=10) on Ego4D. Beyond this window, performance gains diminish, likely due to the introduction of noise from unrelated future actions. The resulting model, dubbed ECOGaze, achieves state-of-the-art performance for causal gaze estimation while being significantly more lightweight and faster than previous transformer-based baselines.

This work provides actionable guidance for the design of real-time egocentric gaze systems. It demonstrates that lightweight causal models can effectively absorb anticipatory signals from future frames during training, allowing them to perform better than models trained only on past and present data. By identifying a bounded optimal temporal window, the authors provide a practical heuristic for developers to balance the trade-off between training complexity and inference accuracy in low-latency applications.

Key Terms

Future-Privileged Supervision

A training technique where a model branch with access to future frames guides a strictly causal student model to learn anticipatory cues.

Strictly Causal

An inference constraint where the model prediction at any time step depends solely on past and present observations, with no access to future frames.

Look-ahead Horizon (H)

The number of future frames or the temporal duration of future context provided to the teacher branch during the training phase.

Global-Local Focusing (GLF)

A module that uses a global query to compute similarity with local patch features, highlighting gaze-relevant regions within a frame.

Egocentric Gaze Estimation

The task of inferring the focus of attention of a camera wearer from first-person video input.

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