EyeTAG: Eye Trajectory-Aware Gaze Estimation

arXiv:2610.00922 · cs.CV, cs.LG · Submitted 2026-10-01 · Read on arXiv

cs.CV, cs.LG

Submitted: 2026-10-01

Updated: 2026-10-01

Comments: Accepted to BMVC 2026

Code: https://github.com/peter8366/EyeTAG

License: http://creativecommons.org/licenses/by/4.0/

The gist: Gaze estimation under natural head-eye motion underpins applications from driver monitoring to human-computer interaction.

Terminology

Accuracy — 11× in this paper · explained in 1 episode
Angular Error — 10× in this paper · explained in 1 episode
Transformer — 10× in this paper · explained in 2 episodes
Ablation — 9× in this paper · explained in 1 episode
Mean Angular Error — 7× in this paper
Embeddings — 5× in this paper · explained in 2 episodes
Aggregation — 4× in this paper · explained in 1 episode
Benchmark — 4× in this paper · explained in 2 episodes
Calibration — 4× in this paper · explained in 7 episodes
Cross-Modal Causal Attention Fusion — 3× in this paper
Dual-Stream Visual Encoder — 3× in this paper
Robustness — 3× in this paper · explained in 5 episodes
Trajectory-Aware Kinematic Encoder — 3× in this paper
Consistency — 2× in this paper · explained in 3 episodes
Gaze Vector Predictor — 2× in this paper
Representation Learning — 2× in this paper · explained in 2 episodes
Temporal Reasoning — 2× in this paper · explained in 2 episodes
Ablation Studies — 1× in this paper · explained in 1 episode
Ablation study — 1× in this paper · explained in 1 episode
Core — 1× in this paper · explained in 2 episodes
Elias — 1× in this paper · explained in 2 episodes
Face and Gesture Recognition — 1× in this paper
Multi-Layer Perceptron — 1× in this paper
National Research Foundation of Korea — 1× in this paper
Over-smoothing — 1× in this paper · explained in 4 episodes
Physical Constraints — 1× in this paper · explained in 7 episodes
Sparse Attention — 1× in this paper · explained in 5 episodes
Trace — 1× in this paper · explained in 2 episodes
Transformer Architecture — 1× in this paper · explained in 2 episodes

Abstract

Gaze estimation under natural head-eye motion underpins applications from driver monitoring to human-computer interaction. Single-frame methods predict each frame independently, so consecutive outputs fluctuate as jitter. Multi-frame methods reduce this, but they learn motion implicitly inside appearance features, so the gaze trajectory is never an explicit variable. We propose EyeTAG (Eye Trajectory-Aware Gaze Estimation), a causal multi-frame framework built around an explicit first-order gaze prior: at each step it differentiates its own recent predictions and feeds the resulting trajectory back as a compact kinematic token. Because differencing is translation-invariant in gaze space, this token carries subject-invariant motion rather than personal gaze offsets. Face and eye streams supply visual evidence, fused by cross-attention and a causal Transformer decoder. EyeTAG reduces the mean angular error by about 1.0 on Gaze360 and performs on par with the strongest baseline on EVE (2.56 vs. 2.58). Within-model ablations, which keep the encoder and the rest of the architecture fixed and vary only the gaze history, show that the differential formulation, rather than temporal context alone, removes the systematic saccade bias that persists even with an absolute gaze-history prior. Our code is available at https://github.com/peter8366/EyeTAG.

Terminology

Abstract

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