How Much Future Helps? A Controlled Study of Future-Privileged Supervision for Causal Egocentric Gaze Estimation
Quick Answer
This paper shows that A controlled study reveals that future-privileged supervision enhances causal egocentric gaze estimation, with optimal performance achieved at 1.7-3.3 seconds look-ahead on EGTEA Gaze+ and 2.7 seconds on Ego4D.
Quick Take
This suggests lightweight causal models can effectively utilize future context for real-time applications.
Key Points
- Future-privileged supervision improves causal gaze prediction consistently across datasets.
- Optimal look-ahead for gaze estimation is 1.7-3.3 seconds on EGTEA Gaze+.
- Ego4D shows peak performance with a 2.7-second look-ahead.
- The study isolates future context impact while maintaining a causal inference architecture.
- Lightweight causal models can absorb future-aware signals for real-time gaze modeling.
Paper Resources
📖 Reader Mode
~2 min readAbstract:Egocentric gaze estimation is commonly studied using models that process the full video with access to future frames, while real-world applications require strictly causal, online prediction. This discrepancy raises key questions: Does future context inherently provide valuable signals for gaze estimation? If so, how much future look-ahead optimally supervises a causal model during training? To investigate, we propose a controlled framework featuring a future-aware branch that accesses a tunable look-ahead horizon during training but is discarded at inference. This design isolates the impact of future context while keeping the inference architecture fixed and strictly causal. Across EGTEA Gaze+ and Ego4D, we find that future-privileged supervision consistently improves causal gaze prediction, confirming its utility. However, performance gains do not increase monotonically with longer look-ahead, but rather peak within a bounded temporal regime. Specifically, optimal performance corresponds to roughly 1.7--3.3 seconds of future context ($H{\in}[5, 10]$) on EGTEA Gaze+ and 2.7 seconds ($H{=}10$) on Ego4D. Our results demonstrate that lightweight causal models can effectively absorb future-aware signals, providing practical guidance for real-time egocentric gaze modeling.
| Comments: | Accepted to the 7th International Workshop on Eye and Gaze in Computer Vision (GAZE 2026), CVPR 2026. Best Paper Award |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2607.01437 [cs.CV] |
| (or arXiv:2607.01437v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2607.01437 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jia Li [view email]
[v1]
Wed, 1 Jul 2026 20:00:12 UTC (7,766 KB)
— Originally published at arxiv.org
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