轻量级眼球追踪方法，仅用面部关键点实现快速校准与部署。
Deployment-Oriented Session-wise Meta-Calibration for Landmark-Based Webcam Gaze Tracking
- 基于关键点的会话自适应校准，用小样本快速生成个性化模型。
- 9点校准后平均误差5.79度，比传统方法低14%，静止头姿下优势更明显。
- 模型仅94万参数，可在浏览器实时运行，适合实际应用部署。
实用化网络摄像头注视追踪不仅受限于误差，还受校准负担、头部运动鲁棒性、会话漂移、运行时开销和浏览器兼容性影响。为此，本文不追求大模型范式，而是聚焦可部署的实用操作点。将基于关键点的注视方向估计建模为会话级自适应：共享几何编码器生成嵌入，可通过少量校准数据对齐新会话。提出E(3)等变元校准注视（EMC-Gaze）方法，仅依赖关键点，结合等变关键点图编码器、局部眼结构、双目强调、辅助3D注视方向监督及可微分的闭式岭校准器，通过周期元训练优化。为减少姿态泄露，引入双视图规范化一致性损失。部署时仅需面部关键点，从短时校准中拟合每会话的岭回归头。在33次会话、100厘米距离的固定注视评估中，9点校准后均方根误差为5.79±1.81度，优于弹性网的6.68±2.34度；静止头姿下差距更大（2.92±0.75度 vs. 4.45±0.30度）。三个各10人受试者外推测试中，仍保持优势（5.66±0.19度 vs. 6.49±0.33度）。在MPIIFaceGaze数据集上，短会话校准下，32-视角模型在16次校准时达8.82±1.21度，1次校准时与弹性网持平，3次起持续领先。导出的眼部编码器仅944,423参数，ONNX格式4.76 MB，Chrome 145中通过ONNX Runtime Web支持12.58/12.58/12.90毫秒（均值/中位数/90百分位）每样本预测。结果表明，EMC-Gaze是校准友好型实用方案，而非对抗重型外观基系统的通用最先进方法。
原文摘要 · Abstract (English)
Practical webcam gaze tracking is constrained not only by error, but also by calibration burden, robustness to head motion and session drift, runtime footprint, and browser use. We therefore target a deployment-oriented operating point rather than the image large-backbone regime. We cast landmark-based point-of-regard estimation as session-wise adaptation: a shared geometric encoder produces embeddings that can be aligned to a new session from a small calibration set. We present Equivariant Meta-Calibrated Gaze (EMC-Gaze), a lightweight landmark-only method combining an E(3)-equivariant landmark-graph encoder, local eye geometry, binocular emphasis, auxiliary 3D gaze-direction supervision, and a closed-form ridge calibrator differentiated through episodic meta-training. To reduce pose leakage, we use a two-view canonicalization consistency loss. The deployed predictor uses only facial landmarks and fits a per-session ridge head from brief calibration. In a fixation-style interactive evaluation over 33 sessions at 100 cm, EMC-Gaze achieves 5.79 +/- 1.81 deg RMSE after 9-point calibration versus 6.68 +/- 2.34 deg for Elastic Net; the gain is larger on still-head queries (2.92 +/- 0.75 deg vs. 4.45 +/- 0.30 deg). Across three subject holdouts of 10 subjects each, EMC-Gaze retains an advantage (5.66 +/- 0.19 deg vs. 6.49 +/- 0.33 deg). On MPIIFaceGaze with short per-session calibration, the eye-focused model reaches 8.82 +/- 1.21 deg at 16-shot calibration, ties Elastic Net at 1-shot, and outperforms it from 3-shot onward. The exported eye-focused encoder has 944,423 parameters, is 4.76 MB in ONNX, and supports calibrated browser prediction in 12.58/12.58/12.90 ms per sample (mean/median/p90) in Chromium 145 with ONNX Runtime Web. These results position EMC-Gaze as a calibration-friendly operating point rather than a universal state-of-the-art claim against heavier appearance-based systems.
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